Metagenomic next-generation sequencing in patients with fever of unknown origin: A comprehensive systematic literature review and meta-analysis

Metagenomic Next-Generation Sequencing (mNGS) holds promise in diagnosing fever of unknown origin (FUO) by detecting diverse pathogens. We systematically reviewed the literature to evaluate mNGS's accuracy, clinical efficacy, and limitations in FUO diagnosis. Nine studies revealed mNGS's positivity rate ranging from 66.7% to 93.5% for bacterial bloodstream infections and systemic infections. Meta-analysis of three studies involving 857 patients, including 354 with FUO, showed a sensitivity of 0.91 (95% CI: 0.87-0.93) and specificity of 0.64 (95% CI: 0.58-0.70). Despite lower specificity, mNGS demonstrated a higher Diagnostic Odds Ratio (DOR) of 17.0 (95% CI: 4.5-63.4) compared to conventional microbiological tests (CMTs) at 4.7 (95% CI: 2.9-7.6). While mNGS offers high sensitivity but low specificity in identifying causative pathogens for FUO, its superior DOR suggests potential for more accurate diagnoses and targeted interventions. Further research is warranted to optimize its clinical application in FUO management.

A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data

Patient safety is a key quality issue for health systems. Healthcare acquired adverse events (AEs) compromise safety and quality; therefore, their reporting and monitoring is a patient safety priority. Although administrative datasets are potentially efficient tools for monitoring rates of AEs, concerns remain over the accuracy of their data. Chart review validation studies are required to explore the potential of administrative data to inform research and health policy. This review aims to present an overview of the methodological approaches and strategies used to validate rates of AEs in administrative data through chart review. This review was conducted in line with the Joanna Briggs Institute methodological framework for scoping reviews. Through database searches, 1054 sources were identified, imported into Covidence, and screened against the inclusion criteria. Articles that validated rates of AEs in administrative data through chart review were included. Data were extracted, exported to Microsoft Excel, arranged into a charting table, and presented in a tabular and descriptive format. Fifty-six studies were included. Most sources reported on surgical AEs; however, other medical specialties were also explored. Chart reviews were used in all studies; however, few agreed on terminology for the study design. Various methodological approaches and sampling strategies were used. Some studies used the Global Trigger Tool, a two-stage chart review method, whilst others used alternative single-, two-stage, or unclear approaches. The sources used samples of flagged charts (n = 24), flagged and random charts (n = 11), and random charts (n = 21). Most studies reported poor or moderate accuracy of AE rates. Some studies reported good accuracy of AE recording which highlights the potential of using administrative data for research purposes. This review highlights the potential for administrative data to provide information on AE rates and improve patient safety and healthcare quality. Nonetheless, further work is warranted to ensure that administrative data are accurate. The variation of methodological approaches taken, and sampling techniques used demonstrate a lack of consensus on best practice; therefore, further clarity and consensus are necessary to develop a more systematic approach to chart reviewing.

The effectiveness of the COVID-19 vaccines in the prevention of post-COVID conditions in children and adolescents: a systematic literature review and meta-analysis

Objective

We performed a systematic literature review and meta-analysis on the effectiveness of coronavirus disease 2019 (COVID-19) vaccination against post-COVID conditions (long COVID) in the pediatric population.

Design

Systematic literature review/meta-analysis.

Methods

We searched PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to August 14, 2023, for studies evaluating the COVID-19 vaccine effectiveness against post-COVID conditions among vaccinated individuals < 21 years old who received at least 1 dose of COVID-19 vaccine. A post-COVID condition was defined as any symptom that was present 4 or more weeks after COVID-19 infection. We calculated the pooled diagnostic odds ratio (DOR) (95% CI) for post-COVID conditions between vaccinated and unvaccinated individuals.

Results

Eight studies with 23,995 individuals evaluated the effect of vaccination on post-COVID conditions, of which 5 observational studies were included in the meta-analysis. The prevalence of children who did not receive COVID-19 vaccines ranged from 65% to 97%. The pooled prevalence of post-COVID conditions was 21.3% among those unvaccinated and 20.3% among those vaccinated at least once. The pooled DOR for post-COVID conditions among individuals vaccinated with at least 1 dose and those vaccinated with 2 doses were 1.07 (95% CI, 0.77-1.49) and 0.82 (95% CI, 0.63-1.08), respectively.

Conclusions

A significant proportion of children and adolescents were unvaccinated, and the prevalence of post-COVID conditions was higher than reported in adults. While vaccination did not appear protective, conclusions were limited by the lack of randomized trials and selection bias inherent in observational studies.

Data quality review of the Brazilian nosocomial infections surveillance system

BACKGROUND

Surveillance of healthcare-associated infections (HAIs) is an essential component of hospital infection prevention and control systems. We aimed to assess the quality of the data compiled by the Brazilian HAI Surveillance System from pediatric (PICUs) and neonatal intensive care units (NICUs), between 2012 and 2021.

METHODS

Data Quality Review, including adherence, completeness, internal consistency, consistency over time, and consistency of population trend, were computed at both national and state levels based on quality metrics from World Health Organization Toolkit. Incidence rates (or incidence density) of ventilator-associated pneumonia (VAP) and central line-associated bloodstream infection (CLABSI) were obtained from the Brazilian National Nosocomial Infections Surveillance (NNIS) system. Data on sepsis-related mortality, spanning the period from 2012 to 2021, were extracted from the Brazilian National Health Service database (DATASUS). Additionally, correlations between sepsis-related mortality and incidence rates of VAP or CLABSI were calculated.

RESULTS

Throughout the majority of the study period, adherence to VAP reporting remained below 75%, exhibiting a positive trend post-2016. Widespread outliers, as well as inconsistencies over time and in population trends, were evident across all 27 states. Only four states maintained consistent adherence levels above 75% for more than 8 years regarding HAI incidence rates. Notably, CLABSI in NICUs boasted the highest reporting adherence among all HAIs, with 148 periods out of 270 (54.8%) exhibiting reporting adherence surpassing 75%. Three states achieved commendable metrics for CLABSI in PICUs, while five states demonstrated favorable results for CLABSI in NICUs.

CONCLUSIONS

While adherence to HAI report is improving among Brazilian states, an important room for improvement in the Brazilian NNIS exists. Additional efforts should be made by the Brazilian government to improve the reliability of HAI data, which could serve as valuable guidance for hospital infection prevention and control policies.

Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data

OBJECTIVE

To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data.

BACKGROUND

Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data.

METHODS

Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively.

RESULTS

Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89.

CONCLUSIONS

Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.

Establishing semi-automated infection surveillance in obstetrics and gynaecology

BACKGROUND

Surveillance is an acknowledged method to decrease nosocomial infections, such as surgical site infections (SSIs). Electronic healthcare records create the opportunity for automated surveillance. While approaches for different types of surgeries and indicators already exist, there are very few for obstetrics and gynaecology.

AIM

To analyse the sensitivity and workload reduction of semi-automated surveillance in obstetrics and gynaecology.

METHODS

In this retrospective, single-centre study at a 1438-bed tertiary care hospital in Germany, semi-automated SSI surveillance using the indicators 'antibiotic prescription', 'microbiological data' and 'administrative data' (diagnosis codes, readmission, post-hospitalization care) was compared with manual analysis and categorization of all patient files. Breast surgeries (BSs) conducted in 2018 and caesarean sections (CSs) that met the inclusion criteria between May 2013 and December 2019 were included. Indicators were analysed for sensitivity, number of analysed procedures needed to identify one case, and potential workload reduction in detecting SSIs in comparison with the control group.

FINDINGS

The reference standard showed nine SSIs in 416 BSs (2.2%). Sensitivities for the indicators 'antibiotic prescription', 'diagnosis code', 'microbiological sample taken', and the combination 'diagnosis code or microbiological sample' were 100%, 88.9%, 66.7% and 100%, respectively. The reference standard showed 54 SSIs in 3438 CSs (1.6%). Sensitivities for the indicators 'collection of microbiological samples', 'diagnosis codes', 'readmission/post-hospitalization care', and the combination of all indicators were 38.9%, 27.8%, 85.2% and 94.4%, respectively.

CONCLUSIONS

Semi-automated surveillance systems may reduce workload by maintaining high sensitivity depending on the type of surgery, local circumstances and thorough digitalization.

Prevalence of hospital-associated infections and its association with discharge destinations and hospital readmissions in Brussels, Belgium, from 2008 to 2020: A hospital-based, cross-sectional study

OBJECTIVES

To examine time trends of hospital-associated infections (HAIs) in people living in the Brussels-Capital Region, and to evaluate the consequences for hospitals and long-term care facilities (LTCFs).

DESIGN

Cross-sectional analyses of yearly hospital administrative data.

SETTING

All Belgian hospitals and discharge destinations, focusing on LTCFs.

PARTICIPANTS

All individuals from the Brussels-Capital Region hospitalized for >1 day throughout Belgium between 2008 and 2020 (N = 1,915,572).

METHODS

We calculated HAI prevalences and then, adjusting for confounders, the odds of being discharged to a LTCF or being readmitted within 30 days postdischarge after an HAI. HAIs included hospital-associated bloodstream infections, hospital-associated urinary tract infections, hospital-associated pneumonia, ventilator-associated pneumonia, and surgical-site infections.

RESULTS

Between 2008 and 2020, we identified 77,004 HAIs. Changes in time trends occurred. We observed a decrease of all HAIs from 2012 to 2014 from 5.17% to 2.19% (P < .001) and an increase from 2019 to 2020 from 3.38% to 4.06% (P < .001). Among patients with HAIs, 24.36% were discharged to LTCFs and 13.51% underwent early readmission. For stays ≥4 days, HAIs were associated with higher odds of LTCF discharge (adjusted odds ratio [aOR], 1.25; 95% confidence interval [CI], 1.22-1.28), but with lesser odds of early readmission (aOR, 0.88; 95% CI, 0.85-0.90).

CONCLUSIONS

Administrative data can be useful to detect HAIs trends, but they seem to underestimate the burden compared to surveillance systems. Risk factors of readmission should be identified during hospital stays to ensure continuity of care. Considering the results from 2020 coinciding with the COVID-19 pandemic, monitoring the impact of HAIs should continue.

Association between hospital onset of infection and outcomes in sepsis patients - A propensity score matched cohort study based on health claims data in Germany

BACKGROUND

Hospital-acquired infections are a common source of sepsis. Hospital onset of sepsis was found to be associated with higher acute mortality and hospital costs, yet its impact on long-term patient-relevant outcomes and costs is unknown.

OBJECTIVE

We aimed to assess the association between sepsis origin and acute and long-term outcomes based on a nationwide population-based cohort of sepsis patients in Germany.

METHODS

This retrospective cohort study used nationwide health claims data from 23 million health insurance beneficiaries. Sepsis patients with hospital-acquired infections (HAI) were identified by ICD-10-codes in a cohort of adult patients with hospital-treated sepsis between 2013 and 2014. Cases without these ICD-10-codes were considered as sepsis cases with community-acquired infection (CAI) and were matched with HAI sepsis patients by propensity score matching. Outcomes included in-hospital/12-month mortality and costs, as well as readmissions and nursing care dependency until 12 months postsepsis.

RESULTS

We matched 33,110 HAI sepsis patients with 28,614 CAI sepsis patients and 22,234 HAI sepsis hospital survivors with 19,364 CAI sepsis hospital survivors. HAI sepsis patients had a higher hospital mortality than CAI sepsis patients (32.8% vs. 25.4%, RR 1.3, p < .001). Similarly, 12-months postacute mortality was higher (37.2% vs. 30.1%, RR=1.2, p < .001). Hospital and 12-month health care costs were 178% and 22% higher in HAI patients than in CAI patients, respectively. Twelve months postsepsis, HAI sepsis survivors were more often newly dependent on nursing care (33.4% vs. 24.0%, RR=1.4, p < .001) and experienced 5% more hospital readmissions (mean number of readmissions: 2.1 vs. 2.0, p < .001).

CONCLUSIONS

HAI sepsis patients face an increased risk of adverse outcomes both during the acute sepsis episode and in the long-term. Measures to prevent HAI and its progression into sepsis may be an opportunity to mitigate the burden of long-term impairments and costs of sepsis, e.g., by early detection of HAI progressing into sepsis, particularly in normal wards; adequate sepsis management and adherence to sepsis bundles in hospital-acquired sepsis; and an improved infection prevention and control.

Development of an automated, general-purpose prediction tool for postoperative respiratory failure using machine learning: A retrospective cohort study

STUDY OBJECTIVE

Postoperative respiratory failure is a major surgical complication and key quality metric. Existing prediction tools underperform, are limited to specific populations, and necessitate manual calculation. This limits their implementation. We aimed to create an improved, machine learning powered prediction tool with ideal characteristics for automated calculation.

DESIGN, SETTING, AND PATIENTS

We retrospectively reviewed 101,455 anesthetic procedures from 1/2018 to 6/2021. The primary outcome was the Standardized Endpoints in Perioperative Medicine consensus definition for postoperative respiratory failure. Secondary outcomes were respiratory quality metrics from the National Surgery Quality Improvement Sample, Society of Thoracic Surgeons, and CMS. We abstracted from the electronic health record 26 procedural and physiologic variables previously identified as respiratory failure risk factors. We randomly split the cohort and used the Random Forest method to predict the composite outcome in the training cohort. We coined this the RESPIRE model and measured its accuracy in the validation cohort using area under the receiver operating curve (AUROC) analysis, among other measures, and compared this with ARISCAT and SPORC-1, two leading prediction tools. We compared performance in a validation cohort using score cut-offs determined in a separate test cohort.

MAIN RESULTS

The RESPIRE model exhibited superior accuracy with an AUROC of 0.93 (95% CI, 0.92-0.95) compared to 0.82 for both ARISCAT and SPORC-1 (P-for-difference < 0.0001 for both). At comparable 80-90% sensitivities, RESPIRE had higher positive predictive value (11%, 95% CI: 10-12%) and lower false positive rate (12%, 95% CI: 12-13%) compared to 4% and 37% for both ARISCAT and SPORC-1. The RESPIRE model also better predicted the established quality metrics for postoperative respiratory failure.

CONCLUSIONS

We developed a general-purpose, machine learning powered prediction tool with superior performance for research and quality-based definitions of postoperative respiratory failure.

The effectiveness of COVID-19 vaccine in the prevention of post-COVID conditions: a systematic literature review and meta-analysis of the latest research

Objective

We performed a systematic literature review and meta-analysis on the effectiveness of coronavirus disease 2019 (COVID-19) vaccination against post-COVID conditions (long COVID) among fully vaccinated individuals.

Design

Systematic literature review/meta-analysis.

Methods

We searched PubMed, Cumulative Index to Nursing and Allied Health, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to June 2, 2023, for studies evaluating the COVID-19 vaccine effectiveness (VE) against post-COVID conditions among fully vaccinated individuals who received two doses of COVID-19 vaccine. A post-COVID condition was defined as any symptom that was present four or more weeks after COVID-19 infection. We calculated the pooled diagnostic odds ratio (DOR) (95% confidence interval) for post-COVID conditions between fully vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% x (1-DOR).

Results

Thirty-two studies with 775,931 individuals evaluated the effect of vaccination on post-COVID conditions, of which, twenty-four studies were included in the meta-analysis. The pooled DOR for post-COVID conditions among fully vaccinated individuals was 0.680 (95% CI: 0.523-0.885) with an estimated VE of 32.0% (11.5%-47.7%). Vaccine effectiveness was 36.9% (23.1%-48.2%) among those who received two doses of COVID-19 vaccine before COVID-19 infection and 68.7% (64.7%-72.2%) among those who received three doses before COVID-19 infection. The stratified analysis demonstrated no protection against post-COVID conditions among those who received COVID-19 vaccination after COVID-19 infection.

Conclusions

Receiving a complete COVID-19 vaccination prior to contracting the virus resulted in a significant reduction in post-COVID conditions throughout the study period, including during the Omicron era. Vaccine effectiveness demonstrated an increase when supplementary doses were administered.

The association of infectious diseases consultation and 30-day mortality rates among veterans with enterococcal bacteraemia: a propensity score-matched retrospective cohort study

OBJECTIVES

Infectious disease consultation (IDC) has been associated with improved outcomes in several infections, but the benefit of IDC among patients with enterococcal bacteraemia has not been fully evaluated.

METHODS

We performed a 1:1 propensity score-matched retrospective cohort study evaluating all patients with enterococcal bacteraemia at 121 Veterans Health Administration acute-care hospitals from 2011 to 2020. The primary outcome was 30-day mortality. We performed conditional logistic regression to calculate the OR to determine the independent association of IDC and 30-day mortality adjusted for vancomycin susceptibility and the primary source of bacteraemia.

RESULTS

A total of 12,666 patients with enterococcal bacteraemia were included; 8400 (63.3%) had IDC, and 4266 (36.7%) did not have IDC. Two thousand nine hundred seventy-two patients in each group were included after propensity score matching. Conditional logistic regression revealed that IDC was associated with a significantly lower 30-day mortality rate compared with patients without IDC (OR = 0.56; 95% CI, 0.50-0.64). The association of IDC was observed irrespective of vancomycin susceptibility, and when the primary source of bacteraemia was a urinary tract infection, or from an unknown primary source. IDC was also associated with higher appropriate antibiotic use, blood culture clearance documentation, and the use of echocardiography.

DISCUSSION

Our study suggests that IDC was associated with improved care processes and 30-day mortality rates among patients with enterococcal bacteraemia. IDC should be considered for patients with enterococcal bacteraemia.

Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning

BACKGROUND

Postoperative infections constitute more than half of all postoperative complications. Surveillance of these complications is primarily done through manual chart review, which is time consuming, expensive, and typically only covers 10% to 15% of all operations. Automated surveillance would permit the timely evaluation of and reporting of all operations.

METHODS

The goal of this study was to develop and validate parsimonious, interpretable models for conducting surveillance of postoperative infections using structured electronic health records data. This was a retrospective study using 30,639 unique operations from 5 major hospitals between 2013 and 2019. Structured electronic health records data were linked to postoperative outcomes data from the American College of Surgeons National Surgical Quality Improvement Program. Predictors from the electronic health records included diagnoses, procedures, and medications. Infectious complications included surgical site infection, urinary tract infection, sepsis, and pneumonia within 30 days of surgery. The knockoff filter, a penalized regression technique that controls type I error, was applied for variable selection. Models were validated in a chronological held-out dataset.

RESULTS

Seven percent of patients experienced at least one type of postoperative infection. Models selected contained between 4 and 8 variables and achieved >0.91 area under the receiver operating characteristic curve, >81% specificity, >87% sensitivity, >99% negative predictive value, and 10% to 15% positive predictive value in a held-out test dataset.

CONCLUSION

Surveillance and reporting of postoperative infection rates can be implemented for all operations with high accuracy using electronic health records data and simple linear regression models.

The effectiveness of coronavirus disease 2019 (COVID-19) vaccine in the prevention of post-COVID-19 conditions: A systematic literature review and meta-analysis

BACKGROUND

Although multiple studies have revealed that coronavirus disease 2019 (COVID-19) vaccines can reduce COVID-19-related outcomes, little is known about their impact on post-COVID-19 conditions. We performed a systematic literature review and meta-analysis on the effectiveness of COVID-19 vaccination against post-COVID-19 conditions (ie, long COVID).

METHODS

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 1, 2019, to April 27, 2022, for studies evaluating COVID-19 vaccine effectiveness against post-COVID-19 conditions among individuals who received at least 1 dose of Pfizer/BioNTech, Moderna, AstraZeneca, or Janssen vaccine. A post-COVID-19 condition was defined as any symptom that was present 3 or more weeks after having COVID-19. Editorials, commentaries, reviews, study protocols, and studies in the pediatric population were excluded. We calculated the pooled diagnostic odds ratios (DORs) for post-COVID-19 conditions between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 - DOR).

RESULTS

In total, 10 studies with 1,600,830 individuals evaluated the effect of vaccination on post-COVID-19 conditions, of which 6 studies were included in the meta-analysis. The pooled DOR for post-COVID-19 conditions among individuals vaccinated with at least 1 dose was 0.708 (95% confidence interval (CI), 0.692-0.725) with an estimated vaccine effectiveness of 29.2% (95% CI, 27.5%-30.8%). The vaccine effectiveness was 35.3% (95% CI, 32.3%-38.1%) among those who received the COVID-19 vaccine before having COVID-19, and 27.4% (95% CI, 25.4%-29.3%) among those who received it after having COVID-19.

CONCLUSIONS

COVID-19 vaccination both before and after having COVID-19 significantly decreased post-COVID-19 conditions for the circulating variants during the study period although vaccine effectiveness was low.

Clinical documentation requirements for the accurate coding of hospital-acquired urinary tract infections in Australia

Aims We evaluated the accuracy of medical coders in distinguishing the aetiology of urinary tract infection according to clinical documentation. Methods The clinical documentation of patients coded as having had a hospital-acquired urinary tract infection from January to June 2020 at two Melbourne hospitals were assessed for community or hospital acquisition. Results We found that 48.89% of cases were inaccurately categorised as hospital-acquired, due to insufficient detail in clinical documentation. Risk factors for hospital-acquired urinary tract infection were present in at least 30% of correctly categorised cases. Conclusions Clinical documentation is not filled out with sufficient detail or in a timely enough manner for clinical coders to distinguish between hospital or community origin.

Development and validation of a model for surveillance of postoperative bleeding complications using structured electronic health records data

BACKGROUND

Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data.

METHODS

Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included. Electronic health records data were linked to American College of Surgeons National Surgical Quality Improvement Program data using personal health identifiers. Electronic health records predictors included diagnosis codes mapped to PheCodes, procedure names, and medications within 30 days after surgery. We defined bleeding events as the transfusion of red blood cell components within 30 days after surgery. The knockoff filter and the lasso were used to develop a model in a training set of operations from January 2013 to March 2017. Performance of each model was tested in a held-out data set of patients who underwent operations from March 2017 to October 2019.

RESULTS

A total of 30,639 patients were included; 1,112 patients (3.6%) had a bleeding event. Eight predictor variables were selected by the knockoff filter. When applied to the test set, specificity was 94%, sensitivity was 94%, area under the curve was 0.97, and accuracy was 93%. Calibration was consistent in lower predicted risk patients, whereas the model slightly overpredicted risk in high-risk patients.

CONCLUSION

We created a parsimonious, accurate model for identifying patients with bleeding complications. This model can be used to augment manual chart review for surveillance and reporting of perioperative bleeding complications, enabling inclusion of all surgeries in quality improvement efforts.

Effect of climate on surgical site infections and anticipated increases in the United States

Surgical site infections (SSI) are one of the most common and costly hospital-acquired infections in the United States. Meteorological variables such as temperature, humidity, and precipitation may represent a neglected group of risk factors for SSI. Using a national private insurance database, we collected admission and follow-up records for National Healthcare Safety Network-monitored surgical procedures and associated climate conditions from 2007 to 2014. We found that every 10 cm increase of maximum daily precipitation resulted in a 1.09 odds increase in SSI after discharge, while every g/kg unit increase in specific humidity resulted in a 1.03 odds increase in SSI risk after discharge. We identified the Southeast region of the United States at highest risk of climate change-related SSI, with an estimated 3% increase in SSI by 2060 under high emission assumptions. Our results describe the effect of climate on SSI and the potential burden of climate-change related SSI in the United States.

Effect of clinical versus administrative data definitions on the epidemiology of C. difficile among hospitalized individuals with IBD: a population-based cohort study

Background

Hospitalization admissions and discharge databases (DAD) using the International Classification of Diseases (ICD) codes are often used to describe the epidemiology of Clostridioides difficile infections (CDI) among those with Inflammatory bowel disease (IBD), even though DAD CDI definition can miss many cases of CDI. There are no data comparing the assessment of the epidemiology of CDI among those with IBD by DAD versus laboratory diagnosis. We used a population-based dataset to determine the effect of using DAD versus laboratory CDI diagnosis on CDI assessment among those with IBD.

Methods

We linked the University of Manitoba IBD Epidemiology Database to the provincial CDI laboratory dataset for the years 2005–2014. Time trends of CDI were assessed using joinpoint analyses. We used stratified logistic regression analysis to assess factors associated with CDI among individuals with IBD.

Results

Time trends of CDI among hospitalized individuals with IBD were similar when using DAD or the laboratory CDI diagnosis. Prior hospital admission and antibiotic exposure were associated with CDI using either of the CDI definitions, 5-ASA use was associated with CDI using DAD but not laboratory diagnosis, whereas corticosteroid exposure was associated with laboratory-based CDI diagnosis. Using laboratory results as gold standard, DAD had a sensitivity and specificity of 75.4% and 99.6% for CDI among those with IBD.

Conclusions

Using ICD codes in the DAD for CDI provides similar epidemiological time trend patterns as identifying CDI in the laboratory dataset. Hence, ICD codes are reliable to determine CDI epidemiology among hospitalized individuals with IBD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02223-y.

Short-term effectiveness of COVID-19 vaccines in immunocompromised patients: A systematic literature review and meta-analysis

OBJECTIVES

We aimed to assess the short-term effectiveness of COVID-19 vaccines among immunocompromised patients to prevent laboratory-confirmed symptomatic COVID-19 infection.

METHODS

Systematic review and meta-analysis. We calculated the pooled diagnostic odds ratio [DOR] (95% CI) for COVID-19 infection between immunocompromised patients and healthy people or those with stable chronic medical conditions. VE was estimated as 100% x (1-DOR). We also investigated the rates of developing anti-SARS-CoV-2 spike protein IgG between the 2 groups.

RESULTS

Twenty studies evaluating COVID-19 vaccine response, and four studies evaluating VE were included in the meta-analysis. The pooled DOR for symptomatic COVID-19 infection in immunocompromised patients was 0.296 (95% CI: 0.108-0.811) with an estimated VE of 70.4% (95% CI: 18.9%- 89.2%). When stratified by diagnosis, IgG antibody levels were much higher in the control group compared to immunocompromised patients with solid organ transplant (pOR 232.3; 95% Cl: 66.98-806.03), malignant diseases (pOR 42.0, 95% Cl: 11.68-151.03), and inflammatory rheumatic diseases (pOR 19.06; 95% Cl: 5.00-72.62).

CONCLUSIONS

We found COVID-19 mRNA vaccines were effective against symptomatic COVID-19 among the immunocompromised patients but had lower VE compared to the controls. Further research is needed to understand the discordance between antibody production and protection against symptomatic COVID-19 infection.

Methicillin-Resistant and Methicillin-Sensitive Staphylococcus aureus Hospitalizations: National Inpatient Sample, 2016-2019

Data from the National Inpatient Sample demonstrate that methicillin-resistant Staphylococcus aureus (MRSA)–related septicemia hospitalizations increased from 1.67 (95% CI, 1.63–1.72) to 1.94 (95% CI, 1.88–2.00; P_trend < .001) discharges per 1000 hospitalizations between 2016 and 2019. Regionally, the trends were similar. Rates of MSSA-related septicemia and pneumonia hospitalizations also increased significantly over this time period.

The long-term effectiveness of coronavirus disease 2019 (COVID-19) vaccines: A systematic literature review and meta-analysis

Background:

Although multiple studies revealed high vaccine effectiveness of coronavirus disease 2019 (COVID-19) vaccines within 3 months after the completion of vaccines, long-term vaccine effectiveness has not been well established, especially after the δ (delta) variant became prominent. We performed a systematic literature review and meta-analysis of long-term vaccine effectiveness.

Methods:

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to November 15, 2021, for studies evaluating the long-term vaccine effectiveness against laboratory-confirmed COVID-19 or COVID-19 hospitalization among individuals who received 2 doses of Pfizer/BioNTech, Moderna, or AstraZeneca vaccines, or 1 dose of the Janssen vaccine. Long-term was defined as >5 months after the last dose. We calculated the pooled diagnostic odds ratio (DOR) with 95% confidence interval for COVID-19 between vaccinated and unvaccinated individuals. Vaccine effectiveness was estimated as 100% × (1 − DOR).

Results:

In total, 16 studies including 17,939,172 individuals evaluated long-term vaccine effectiveness and were included in the meta-analysis. The pooled DOR for COVID-19 was 0.158 (95% CI: 0.157-0.160) with an estimated vaccine effectiveness of 84.2% (95% CI, 84.0- 84.3%). Estimated vaccine effectiveness against COVID-19 hospitalization was 88.7% (95% CI, 55.8%–97.1%). Vaccine effectiveness against COVID-19 during the δ variant period was 61.2% (95% CI, 59.0%–63.3%).

Conclusions:

COVID-19 vaccines are effective in preventing COVID-19 and COVID-19 hospitalization across a long-term period for the circulating variants during the study period. More observational studies are needed to evaluate the vaccine effectiveness of third dose of a COVID-19 vaccine, the vaccine effectiveness of mixing COVID-19 vaccines, COVID-19 breakthrough infection, and vaccine effectiveness against newly emerging variants.

The short-term effectiveness of coronavirus disease 2019 (COVID-19) vaccines among healthcare workers: a systematic literature review and meta-analysis

Objective:

Healthcare workers (HCWs) are at risk of COVID-19 due to high levels of SARS-CoV-2 exposure. Thus, effective vaccines are needed. We performed a systematic literature review and meta-analysis on COVID-19 short-term vaccine effectiveness among HCWs.

Methods:

We searched PubMed, CINAHL, EMBASE, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science from December 2019 to June 11, 2021, for studies evaluating vaccine effectiveness against symptomatic COVID-19 among HCWs. To meta-analyze the extracted data, we calculated the pooled diagnostic odds ratio (DOR) for COVID-19 between vaccinated and unvaccinated HCWs. Vaccine effectiveness was estimated as 100% × (1 − DOR). We also performed a stratified analysis for vaccine effectiveness by vaccination status: 1 dose and 2 doses of the vaccine.

Results:

We included 13 studies, including 173,742 HCWs evaluated for vaccine effectiveness in the meta-analysis. The vast majority (99.9%) of HCWs were vaccinated with the Pfizer/BioNTech COVID-19 mRNA vaccine. The pooled DOR for symptomatic COVID-19 among vaccinated HCWs was 0.072 (95% confidence interval [CI], 0.028–0.184) with an estimated vaccine effectiveness of 92.8% (95% CI, 81.6%–97.2%). In stratified analyses, the estimated vaccine effectiveness against symptomatic COVID-19 among HCWs who had received 1 dose of vaccine was 82.1% (95% CI, 46.1%–94.1%) and the vaccine effectiveness among HCWs who had received 2 doses was 93.5% (95% CI, 82.5%–97.6%).

Conclusions:

The COVID-19 mRNA vaccines are highly effective against symptomatic COVID-19, even with 1 dose. More observational studies are needed to evaluate the vaccine effectiveness of other COVID-19 vaccines, COVID-19 breakthrough after vaccination, and vaccine efficacy against new variants.

Does public report card matter? A 10-year interrupted time series analysis on total knee replacement

BACKGROUND

There is a lack of evidence that shows whether a report card can improve health outcomes in terms of infection rates or unscheduled readmission by using rigorous methods to evaluate its impact.

METHODS

We used the National Health Insurance Administration's claims database from 1 January 2004 to 30 December 2013 and a time series analysis to evaluate the impact of the quality report card initiative on three negative outcomes of total knee replacement for each quarter of the year, including the rates of superficial infection of a knee replacement, deep infection of knee arthroplasty and unplanned readmissions for surgical site infection.

RESULTS

These negative outcomes (original scale) do not show significant decreases in terms of superficial infection (-0.05‰, -0.63 to 0.53‰, P = 0.87), deep infection (-0.003‰, -0.19 to 0.18‰, P = 0.97) and unscheduled readmission (0.02‰, -0.21 to 0.25‰, P = 0.88).

CONCLUSION

The total knee replacement public report card initiative did not improve the rate of infection and unscheduled readmission for surgical site infection. This report card in Taiwan should involve physicians' participation in the design and be tailored to be suitable for reading by patients in order to further enhance the chance of improvement in these negative outcomes.

Predicting ventilator-associated pneumonia with machine learning

Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.

Decreased risk of non-infectious anterior uveitis with statin therapy in a large healthcare claims database

PURPOSE

The purpose of this study is to determine if statin therapy decreases the incidence of non-infectious uveitis (NIU) using a retrospective cohort study.

METHODS

Patients enrolled in a national insurance plan who initiated statin (n = 711,734, statin cohort) or other lipid-lowering therapy (n = 148,044, non-statin cohort) were observed for NIU development. Incident NIU in the primary analysis was defined as a new diagnosis code for NIU followed by a second instance of a NIU code within 120 days. For the secondary outcome definition, a corticosteroid prescription or code for an ocular corticosteroid injection within 120 days of the NIU diagnosis code was used instead of the second NIU diagnosis code. Estimation of NIU incidence used multivariable Cox proportional hazards regression. The proportional hazards assumption was satisfied by creating two time periods of analysis, ≤ 150 and > 150 days. Subanalyses were performed by anatomic subtype.

RESULTS

Overall, the primary outcome occurred 541 times over 690,465 person-years in the statin cohort and 103 times over 104,301 person-years in the non-statin cohort. No associations were seen in the ≤ 150-day analyses (p > 0.20 for all comparisons). However, after 150 days, the statin cohort was less likely to develop any uveitis [hazard ratio (HR) = 0.70, 95% confidence interval (CI): 0.51-0.97, P = 0.03] in the primary outcome analysis, but did not meet significance for the secondary outcome (HR = 0.85, 95% CI: 0.63-1.15, P = 0.30). Similarly, in the anatomic subtype analysis, after 150 days, the statin cohort was less likely to develop anterior uveitis (HR = 0.67, 95% CI: 0.47-0.97, P = 0.03) in the primary analysis, but the association did not reach significance for the secondary outcome (HR = 0.82, 95% CI: 0.56-1.20, P = 0.31).

CONCLUSION

Our results suggest that statin therapy for > 150 days decreases the incidence of NIU.

Evaluation of a prospective adverse event reporting system in interventional radiology

AIM

To assess the performance of a prospective adverse event (AE) reporting system.

MATERIALS AND METHODS

Four hundred and seventy-one consecutive arterial procedures were performed in 465 patients (median age, 65 years; interquartile range, 54-77; 276 men) over 2 years by four interventional radiologists at a single centre where clinical follow-up was not performed routinely by interventional radiology (IR). AEs were reported prospectively using a radiology information system or in interventional radiologists' electronic records and combined in a departmental listing of adverse events (DLAE). A retrospective medical record review was performed to identify a reference standard list of AEs for this observational cohort study. AEs were graded according to the Society of Interventional Radiology AE classification system. Descriptive statistics were calculated for the performance of the DLAE. A model comparing the rate of reporting of AEs with and without integration of clinical follow-up was tested for significance.

RESULTS

Thirty-eight of the 471 (8%) IR procedures had an AE according to the reference standard. The DLAE identified 20/38 (53%) of AEs (K=0.67 [good agreement], 95% confidence interval [CI] agreement=0.53-0.81; p=0.0001; sensitivity 52.6% [95% CI, 36-69%], specificity 100% [95% CI, 99-100%], positive predictive value [PPV] 100%, negative predictive value [NPV] 96 [95% CI, 94.5-97%], accuracy 96% [95% CI, 94-97%]). The performance of the AE reporting system will improve with integration of clinical follow-up (p=0.0015).

CONCLUSION

A prospective AE reporting system without clinical integration will not detect all procedure complications.

Factors affecting the quality of diagnosis coding data with a triangulation view: A qualitative study

OBJECTIVE

The most important challenge in utilizing medical record codes is the quality of coding data. The present study aims to investigate factors affecting the quality of diagnosis coding from different aspects covering different stakeholders in a multi-dimensional approach.

METHODS

First, we used Conventional Content Analysis to maximally gather all effective factors. As such, semi-structured interviews were conducted with medical record coders (N = 32) at the referral hospitals in Mashhad, Iran. Second, 86 hospital staff members from 25 provinces were surveyed using a web-based questionnaire. Finally, a focus group discussion was conducted among coders (N = 18) in different hospitals across the country.

RESULTS

In general, the barriers to quality of inpatient record coding can be classified into three categories: (I) physician-related, (II) coder-related, and (III) managerial, financial and administrative factors.

CONCLUSION

A triangulation view (related to coders, physicians as well as managerial, financial and administrative dimensions) could be used to identify the barriers affecting the quality of diagnosis coding data. The results of this study may help policymakers in development and implementation of appropriate strategies and effective interventions to improve the quality of clinical coding.

Evaluation of the accuracy of diagnostic coding for influenza compared to laboratory results: the availability of test results before hospital discharge facilitates improved coding accuracy

Background

Assessing the accuracy of diagnostic coding is essential to ensure the validity and reliability of administrative coded data. The aim of the study was to evaluate the accuracy of assigned International Classification of Diseases version 10-Australian Modification (ICD-10-AM) codes for influenza by comparing with patients’ results of their polymerase chain reaction (PCR)-based laboratory tests.

Method

A retrospective study was conducted across seven public hospitals in New South Wales, Australia. A total of 16,439 patients who were admitted and tested by either cartridge-based rapid PCR or batched multiplex PCR between January 2016 and December 2017 met the inclusion criteria. We calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of ICD-10-AM coding using laboratory results as a gold standard. Separate analyses were conducted to determine whether the availability of test results at the time of hospital discharge influenced diagnostic coding accuracy.

Results

Laboratory results revealed 2759 positive influenza cases, while ICD-10-AM coding identified 2527 patients. Overall, 13.7% (n = 378) of test positive patients were not assigned an ICD-10-AM code for influenza. A further 5.8% (n = 146) patients with negative test results were incorrectly assigned an ICD-10-AM code for influenza. The sensitivity, specificity, PPV and NPV of ICD-10-AM coding were 93.1%; 98.9%; 94.5% and 98.6% respectively when test results were received before discharge and 32.7%; 99.2%; 87.8% and 89.8% respectively when test results were not available at discharge. The sensitivity of ICD-10-AM coding varied significantly across hospitals. The use of rapid PCR or hospitalisation during the influenza season were associated with greater coding accuracy.

Conclusion

Although ICD-10-AM coding for influenza demonstrated high accuracy when laboratory results were received before discharge, its sensitivity was substantially lower for patients whose test results were not available at discharge. The timely availability of laboratory test results during the episode of care could contribute to improved coding accuracy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01531-9.

Spatial Patterns in Hospital-Acquired Infections in Portugal (2014-2017)

BACKGROUND

Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide.

AIM

To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal.

METHODS

Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile, nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters.

RESULTS

A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs (n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups.

CONCLUSION

The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation.

Risk of Non-infectious Uveitis with Metformin Therapy in a Large Healthcare Claims Database

PURPOSE

To determine if metformin is associated with noninfectious uveitis (NIU).

METHODS

Patients in an insurance claims database who initiated metformin (n = 359,139) or other oral anti-diabetic medications (n = 162,847) were followed for NIU development. Both cohort and case-control analyses were performed to assess differing exposure lengths using Cox and conditional logistic regression, respectively.

RESULTS

The hazard ratio (HR) for incident NIU was not significantly different between the metformin and non-metformin cohorts [HR = 1.19, 95% Confidence Interval (CI): 0.92-1.54, P = .19]. The case control analysis similarly showed no association between any metformin use 2 years before the outcome date and NIU [odds ratio (OR) = 0.64, 95% CI: 0.39-1.04, P = .07]. However, there was a protective 20 association between cumulative metformin duration [(445-729 days) adjusted OR (aOR) = 0.49, 95% CI: 0.27-0.90, P = .02] and dosage (>390,000 mg aOR = 0.44, 95% CI: 0.25-0.78, P = .001) compared with no metformin use.

CONCLUSIONS

Our results suggest metformin use for longer durations may be protective of NIU onset.

Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research

Objective:

To develop a fully automated algorithm using data from the Veterans’ Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies.

Design:

Retrospective cohort study.

Setting:

This study was conducted in 11 VA hospitals.

Participants:

Patients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort).

Methods:

Relevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans’ Affairs Surgical Quality Improvement Program (VASQIP).

Results:

Overall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively.

Conclusions:

Considering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.

Antibiotic use and presumptive pathogens in the Veterans Affairs Healthcare System

BACKGROUND

Empirical antibiotic use is common in the hospital. Here, we characterize patterns of antibiotic use, infectious diagnoses, and microbiological lab results among hospitalized patients and aim to quantify the proportion of antibiotic use that is potentially attributable to specific bacterial pathogens.

METHODS

We conducted an observational study using electronic health records from acute care facilities in the United States Veterans Affairs Healthcare System. From October 2017 to September 2018, 482,381 hospitalizations for 332,657 unique patients that met all criteria were included. At least one antibiotic was administered at 202,037 (41.9%) of included hospital stays. We measured frequency of antibiotic use, microbiological specimen collection, and bacterial isolation by diagnosis category and antibiotic group. A tiered system based on specimen collection sites and diagnoses was used to attribute antibiotic use to presumptive causative organisms.

RESULTS

Specimens were collected at 130,012 (64.4%) hospitalizations with any antibiotic use, and at least one bacterial organism was isolated at 35.1% of these stays. Frequency of bacterial isolation varied widely by diagnosis category and antibiotic group. Under increasingly lenient criteria, 10.2% to 31.4% of 974,733 antibiotic days-of-therapy could be linked to a potential bacterial pathogen.

CONCLUSIONS

Overall, the vast majority of antibiotic use could be linked to either an infectious diagnosis or microbiological specimen. Nearly half of antibiotic use occurred when there was a specimen collected but no bacterial organism identified, underscoring the need for rapid and improved diagnostics to optimize antibiotic use.

Pediatric Ambulatory Central Line-Associated Bloodstream Infections

BACKGROUND

Inpatient pediatric central line-associated bloodstream infections (CLABSIs) cause morbidity and increased health care use. Minimal information exists for ambulatory CLABSIs despite ambulatory central line (CL) use in children. In this study, we identified ambulatory pediatric CLABSI incidence density, risk factors, and outcomes.

METHODS

Retrospective cohort with nested case-control study at 5 sites from 2010 through 2015. Electronic queries were used to identify potential cases on the basis of administrative and laboratory data. Chart review was used to confirm ambulatory CL use and adjudicated CLABSIs. Bivariate followed by multivariable backward logistic regression was used to identify ambulatory CLABSI risk factors.

RESULTS

Queries identified 4600 potentially at-risk children; 1658 (36%) had ambulatory CLs. In total, 247 (15%) patients experienced 466 ambulatory CLABSIs with an incidence density of 0.97 CLABSIs per 1000 CL days. Incidence density was highest among patients with tunneled externalized catheters versus peripherally inserted central catheters and totally implanted devices: 2.58 CLABSIs per 1000 CL days versus 1.46 vs 0.23, respectively (P < .001). In a multivariable model, clinic visit (odds ratio [OR] 2.8; 95% confidence interval [CI]: 1.4-5.5) and low albumin (OR 2.3; 95% CI: 1.2-4.3) were positively associated with CLABSI, and prophylactic antimicrobial agents for underlying conditions within the preceding 30 days (OR 0.22; 95% CI: 0.12-0.40) and operating room CL placement (OR 0.36; 95% CI: 0.16-0.79) were inversely associated with CLABSI. A total of 396 patients (85%) were hospitalized because of ambulatory CLABSI with an 8-day median length of stay (interquartile range 5-13).

CONCLUSIONS

Ambulatory pediatric CLABSI incidence density is appreciable and associated with health care use. CL type, patients with low albumin, prophylactic antimicrobial agents, and placement setting may be targets for reduction efforts.

Utility of the Current Procedural Terminology Codes for Prophylactic Stabilization for Defining Metastatic Femur Disease

Introduction:

Cohorts from the electronic health record are often defined by the Current Procedural Terminology (CPT) codes. The error prevalence of CPT codes for patients receiving surgical treatment of metastatic disease of the femur has not been investigated, and the predictive value of coding ontologies to identify patients with metastatic disease of the femur has not been adequately discussed.

Methods:

All surgical cases at a single academic tertiary institution from 2010 through 2015 involving prophylactic stabilization of the femur or fixation of a pathologic fracture of the femur were identified using the CPT and International Classification of Disease (ICD) codes. A detailed chart review was conducted to determine the procedure performed as documented in the surgical note and the patient diagnosis as documented in the pathology report, surgical note, and/or office visit notes.

Results:

We identified 7 CPT code errors of 171 prophylactic operations (4.1%) and one error of 71 pathologic fracture fixation s(1.4%). Of the 164 prophylactic operations that were coded correctly, 87 (53.0%) had metastatic disease. Of the 70 pathologic operations that were coded correctly, 41 (58%) had metastatic disease.

Discussion:

The error prevalence was low in both prophylactic stabilization and pathologic fixation groups (4.1% and 1%, respectively). The structured data (CPT and ICD-9 codes) had a positive predictive value for patients having metastatic disease of 53% for patients in the prophylactic stabilization group and 58% for patients in the pathologic fixation group. The CPT codes and ICD codes assessed in this analysis do provide a useful tool for defining a population in which a moderate proportion of individuals have metastatic disease in the femur at an academic medical center. However, verification is necessary.

Administrative coding methods impact surgical site infection rates

We performed a retrospective analysis of the impact of using the International Classification of Diseases, Tenth Revision procedure coding system (ICD-10) or current procedural terminology (CPT) codes to calculate surgical site infection (SSI) rates. Denominators and SSI rates vary depending on the coding method used. The coding method used may influence interhospital performance comparisons.

Analysis of the Risk Factors for Nosocomial Bacterial Infection in Patients with COVID-19 in a Tertiary Hospital

Background

Infection surveillance and risk factor analysis are among the most important prerequisites for the prevention and treatment of nosocomial bacteria infections, which are the demands for both infected and non-infected patients.

Purpose

To explore the risk factors for nosocomial bacterial infection of patients with COVID-19, and further to provide a theoretical basis for scientific prevention and control of nosocomial bacterial infection.

Methods

Between 10 January 2020 and 9 March 2020, we collected data of 212 patients with COVID-19 and then explored the influence of age, gender, length of stay, use of ventilator, urinary catheterization, central venous catheterization, white blood cell (WBC) count and procalcitonin on the nosocomial bacterial infection of patients with COVID-19 by a retrospective study.

Results

There were 212 confirmed cases of COVID-19, of which 31 cases had nosocomial bacterial infections, with an incidence of 14.62%. The most common types of nosocomial bacterial infections were lower respiratory tract (12 cases, 38.71%), which was the most frequent site, followed by urinary tract (10 cases, 32.26%), blood stream (7 cases, 22.58%), upper respiratory tract (1 case, 3.23%) and gastrointestinal tract infection (1 case, 3.23%). The incidence of nosocomial bacterial infection was significantly correlated with age, arteriovenous catheterization, urinary catheterization, WBC count and procalcitonin. Moreover, multivariate analysis confirmed that WBC (OR 8.38, 95% CI 1.07 to 65.55), procalcitonin (OR 4.92, 95% CI 1.39 to 17.33) and urinary catheterization (OR 25.38, 95% CI 5.09 to 126.53) were independent risk factors for the nosocomial bacterial infection of patients with COVID-19.

Conclusion

Understanding the risk factors for nosocomial bacterial infection of patients with COVID-19 and strengthening the monitoring of various susceptible factors are helpful to control the occurrence of nosocomial bacterial infection in the COVID-19 isolation wards.

How accurately is hospital acquired pneumonia documented for the correct assignment of a hospital acquired complication (HAC)?

BACKGROUND

In 2016, the Australian Commission on Safety and Quality in Healthcare (ACSQHC) released a list of 16 categories of potentially preventable, high impact hospital-acquired complications (HAC) identified by using administrative coded data (ACD). An important category are hospital-acquired infections (HAI). Within this category, hospital-acquired pneumonia (HAP) is among the most frequent complications documented. There are no published studies concerning the current ACSQHC approach to HAI surveillance using ACD and no pneumonia-specific ACD studies reported from Australia. Published work indicates that ACD detection of HAP has low a sensitivity and positive predictive value (PPV). The current study was designed to examine whether coders correctly reflected the documentation of HAP that was present in the medical record and also evaluated the medical documentation that was present.

METHODS

One hundred patients with ACD encoded HAP were selected for review, drawn from admissions to 2 Hunter New England Health hospitals during 2017. Patient records and the eMR were reviewed by two medical officers to assess medical and radiological documentation of pneumonia. The district coding manager reviewed the accuracy of coding of a subset of 23 cases where medical review had not located documented evidence of HAP.

RESULTS

Of the 100 reviewed cases, the median patient age was 75 years (range 0-95 years) with 3% under 16 years of age. Twenty one were intensive care-associated of which 13 were associated with ventilation. In 23 cases the documentation was disputed and a secondary review took place - the coding manager confirmed coding changes in 14 of these 23 cases.

CONCLUSIONS

This study found that administrative coded data of HAP, utilizing the ACSQHC method reliably reflected the available documentation with a PPV of 86% (95% binomial exact confidence interval 77-92%), much higher than documented by previous ACD studies. The actual documentation of pneumonia by medical staff frequently used the non-specific term 'lower respiratory infection (LRTI)' which we recommend to be avoided. Radiological confirmation was absent in one third of cases. We recommend the adoption of a medical note template checklist for HAP to prompt clinicians with the accepted diagnostic criteria. We also recommend documenting a reason as to why any antibiotic has been commenced in a hospitalized patient in accord with the ACSQHC Antimicrobial Stewardship Clinical Care Standard.

Characterizing Clostridioides difficile infections and hospital exposures in California using surveillance and administrative data, 2014-2015

OBJECTIVE

To evaluate a method to identify hospitals contributing to Clostridioides difficile infections (CDI) at subsequent hospitalizations.

DESIGN

Retrospective cohort study.

METHODS

We merged 2014-2015 National Healthcare Safety Network (NHSN) inpatient CDI laboratory-identified events with hospital patient discharge data. For patients with incident community-onset CDI (CO CDI), we identified immediately preceding admissions (within 12 weeks) unrelated to CDI at different (exposure) hospitals. We calculated an exposure rate, and we selected hospitals with the highest (90th-100th percentile) rates by hospital type and compared these rates with reported standardized infection ratios (SIR) for CDI.

RESULTS

We successfully matched 44,691 of 58,842 NHSN CDI records (76.0%) with a hospital discharge record. Among 36,215 unique matched records, 5,234 (14.5%) had an admission not related to CDI within 12 weeks prior to an incident CO CDI event, and 1,574 of these admissions (30.1%) occurred in a different hospital. For 33 hospitals with an exposure ranking within the 90th-100th percentile, CDI SIRs for 22 (66.7%) were not significantly different; 3 (9.1%) were lower; and 8 (24.2%) were higher than the national baseline. Also, 12 (36.4%) had an SIR ≤1.0.

CONCLUSIONS

The identification of high-ranked exposure hospitals presents an alternative to SIR for measuring the contribution of hospitals to the CDI burden across the continuum of care. Further exploration of the potential factors leading to high exposure rank, such as antibiotic use and infection control practices, is indicated and may inform CDI prevention outreach to healthcare facilities and provider networks in California and elsewhere.

Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty-A multicenter study

OBJECTIVE

Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance.

DESIGN

Multicenter retrospective cohort study.

METHODS

Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance.

RESULTS

Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection.

CONCLUSIONS

This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.

Positive Predictive Values of 2 Algorithms for Identifying Patients with Intravenous Drug Use-Associated Endocarditis Using Administrative Data

Background

Prior studies have used International Classification of Disease (ICD) diagnosis codes in administrative data to identify patients with infective endocarditis (IE) associated with intravenous drug use (IVDU). Little is known about the accuracy of ICD codes for IVDU-IE.

Methods

We used 2 previously described algorithms to identify patients with potential IVDU-IE admitted to 125 Veterans Administration hospitals from January 2010 through December 2018. Algorithm A identified patients with concurrent ICD-9/10 codes for IE and drug use during the same admission. Algorithm B identified patients with drug use coded either during the IE admission or during outpatient or other visits within 6 months of admission. We reviewed 400 randomly selected patient charts to determine the positive predictive value (PPV) of each algorithm for clinical documentation of IE, any drug use, IVDU, and IVDU-IE, respectively.

Results

Algorithm A identified 788 patients, and B identified 1314 patients, a 68% increase. PPVs were high for clinical documentation of diagnoses of IE (86.5% for A and 82.6% for B) and any drug use (99.0% and 96.3%). PPVs were lower for documented IVDU (74.5% and 64.1%) and combined diagnoses of IVDU-IE (65.0% and 55.2%), partly because of a lack of ICD codes specific to IVDU. Among patients identified by algorithm B but not A, 72% had clinical documentation of drug use during the IE admission, indicating a failure of algorithm A to capture cases due to incomplete recording of inpatient ICD codes for drug use.

Conclusions

There is need for improved algorithms for IVDU-IE surveillance during the ongoing opioid epidemic.

Identification of postoperative complications using electronic health record data and machine learning

BACKGROUND

Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR).

METHODS

We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors.

RESULTS

Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93.

CONCLUSIONS

Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution.

Risk of Noninfectious Uveitis with Female Hormonal Therapy in a Large Healthcare Claims Database

PURPOSE

To determine if female hormonal therapy (FHT) increases the incidence of noninfectious uveitis.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Women exposed to FHT and matched women unexposed to FHT enrolled in a national insurance plan.

METHODS

Estimation of noninfectious uveitis incidence used multivariable Cox proportional hazards regression. To account for differences between the exposed and unexposed cohorts, a propensity score for being prescribed FHT was created using logistic regression, and inverse probability of treatment weighting was performed.

MAIN OUTCOME MEASURES

Incidence of noninfectious uveitis. For the primary outcome, incident noninfectious uveitis was defined as a new diagnosis code for noninfectious uveitis followed by a second instance of a noninfectious uveitis code within 120 days. For the alternative outcome definition, a corticosteroid prescription or code for an ocular corticosteroid injection within 120 days of the uveitis diagnosis code was used instead of the second uveitis diagnosis code.

RESULTS

There were 217 653 women exposed to FHT and 928 408 women not unexposed to FHT. For the primary outcome, the hazard ratio (HR) for incident noninfectious uveitis was not significantly different between the FHT and unexposed cohorts (HR, 0.99; 95% confidence interval [CI], 0.83-1.17; P = 0.87). With the alternative outcome definition, the FHT cohort was more likely to develop uveitis (HR, 1.21; 95% CI, 1.04-1.41; P = 0.01). When examined by anatomic subtype, for anterior uveitis there was a greater likelihood of incident uveitis in the exposed cohort (HR, 1.23; 95% CI, 1.05-1.45; P = 0.01) for the alternative outcome definition but not for the primary outcome. With age stratification, women exposed to FHT aged ≥45 years at the time of FHT prescription were more likely to develop uveitis (HR, 1.23; 95% CI, 1.03-1.47; P = 0.03) for the alternative outcome definition. A similar HR (1.22) was seen for women aged ≤44 years at the time of prescription, but this association did not meet statistical significance (P = 0.20).

CONCLUSIONS

Exposure to FHT increases the rate of incident noninfectious uveitis when uveitis is defined on the basis of both diagnostic codes and documentation of corticosteroid treatment. However, the risk is modest and FHT is likely safe with regard to noninfectious uveitis risk in the majority of patients exposed to these drugs.

Validation of an algorithm based on administrative data to detect new onset of atrial fibrillation after cardiac surgery

Introduction

Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery associated with important morbidity, mortality, and costs. To assess the effectiveness of preventive interventions, an important prerequisite is to have access to accurate measures of POAF incidence. The aim of this study was to develop and validate such a measure.

Methods

A validation study was conducted at two large Canadian university health centers. First, a random sample of 976 (10.4%) patients who had cardiac surgery at these sites between 2010 and 2016 was generated. Then, a reference standard assessment of their medical records was performed to determine their true POAF status on discharge (positive/negative). The accuracy of various algorithms combining diagnostic and procedure codes from: 1) the current hospitalization, and 2) hospitalizations up to 6 years before the current hospitalization was assessed in comparison with the reference standard. Overall and site-specific estimates of sensitivity, specificity, positive (PPV), and negative (NPV) predictive values were generated, along with their 95%CIs.

Results

Upon manual review, 324 (33.2%) patients were POAF-positive. Our best-performing algorithm combining data from both sites used a look-back window of 6 years to exclude patients previously known for AF. This algorithm achieved 70.4% sensitivity (95%CI: 65.1–75.3), 86.0% specificity (95%CI: 83.1–88.6), 71.5% PPV (95%CI: 66.2–76.4), and 85.4% NPV (95%CI: 82.5–88.0). However, significant site-specific differences in sensitivity and NPV were observed.

Conclusion

An algorithm based on administrative data can identify POAF patients with moderate accuracy. However, site-specific variations in coding practices have significant impact on accuracy.

Incidence and Outcomes Associated With Clostridium difficile Infections: A Systematic Review and Meta-analysis

IMPORTANCE

An understanding of the incidence and outcomes of Clostridium difficile infection (CDI) in the United States can inform investments in prevention and treatment interventions.

OBJECTIVE

To quantify the incidence of CDI and its associated hospital length of stay (LOS) in the United States using a systematic literature review and meta-analysis.

DATA SOURCES

MEDLINE via Ovid, Cochrane Library Databases via Wiley, Cumulative Index of Nursing and Allied Health Complete via EBSCO Information Services, Scopus, and Web of Science were searched for studies published in the United States between 2000 and 2019 that evaluated CDI and its associated LOS.

STUDY SELECTION

Incidence data were collected only from multicenter studies that had at least 5 sites. The LOS studies were included only if they assessed postinfection LOS or used methods accounting for time to infection using a multistate model or compared propensity score-matched patients with CDI with control patients without CDI. Long-term-care facility studies were excluded. Of the 119 full-text articles, 86 studies (72.3%) met the selection criteria.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers performed the data abstraction and quality assessment. Incidence data were pooled only when the denominators used the same units (eg, patient-days). These data were pooled by summing the number of hospital-onset CDI incident cases and the denominators across studies. Random-effects models were used to obtain pooled mean differences. Heterogeneity was assessed using the I2 value. Data analysis was performed in February 2019.

MAIN OUTCOMES AND MEASURES

Incidence of CDI and CDI-associated hospital LOS in the United States.

RESULTS

When the 13 studies that evaluated incidence data in patient-days due to hospital-onset CDI were pooled, the CDI incidence rate was 8.3 cases per 10 000 patient-days. Among propensity score-matched studies (16 of 20 studies), the CDI-associated mean difference in LOS (in days) between patients with and without CDI varied from 3.0 days (95% CI, 1.44-4.63 days) to 21.6 days (95% CI, 19.29-23.90 days).

CONCLUSIONS AND RELEVANCE

Pooled estimates from currently available literature suggest that CDI is associated with a large burden on the health care system. However, these estimates should be interpreted with caution because higher-quality studies should be completed to guide future evaluations of CDI prevention and treatment interventions.

Direct cost of health care for individuals with community associated Clostridium difficile infections: A population-based cohort study

Background

Even though the incidence of community-acquired Clostridium difficile infection (CDI) is reported to be increasing, few studies have reported on the healthcare costs of community-acquired CDI. We estimated cost of care for individuals with community-associated CDI and compared with that for matched controls without CDI in the time period of six months before to one year after CDI.

Methods

All individuals in the province of Manitoba, diagnosed with CDI between July 2005 and March 2015 were matched up to 4 individuals without CDI. Health care utilization and direct costs resulting from hospitalizations, physician reimbursement claims and prescriptions were determined from the population based provincial databases. Quantile regressions were performed to determine predictors of cost of individuals with community associated CDI.

Results

Of all CDIs, 30–40% in each period of the study had community-associated CDI; of which 12% were recurrent CDIs. The incremental median and 90^th percentile cost of care for individuals with community-associated CDI was $800 and $16,000 respectively in the six months after CDI diagnosis. After adjustment for age, co-morbidities, sex, socioeconomic status and magnitude of health care utilization prior to CDI, the median incremental cost for recurrent CDI was $1,812 and that for a subsequent episode of CDI was $3,139 compared to those with a single community-associated CDI episode. The median cost for a prescription of Vancomycin was $316 (IQR 209–489).

Conclusions

Health care costs of an episode of community-associated CDI have been much more than the cost of antibiotic treatment. Our study provides population-based data for formal cost effectiveness analysis for use of newer treatments for community-associated CDI.

'Caveat emptor': the cautionary tale of endocarditis and the potential pitfalls of clinical coding data-an electronic health records study

BACKGROUND

Diagnostic codes from electronic health records are widely used to assess patterns of disease. Infective endocarditis is an uncommon but serious infection, with objective diagnostic criteria. Electronic health records have been used to explore the impact of changing guidance on antibiotic prophylaxis for dental procedures on incidence, but limited data on the accuracy of the diagnostic codes exists. Endocarditis was used as a clinically relevant case study to investigate the relationship between clinical cases and diagnostic codes, to understand discrepancies and to improve design of future studies.

METHODS

Electronic health record data from two UK tertiary care centres were linked with data from a prospectively collected clinical endocarditis service database (Leeds Teaching Hospital) or retrospective clinical audit and microbiology laboratory blood culture results (Oxford University Hospitals Trust). The relationship between diagnostic codes for endocarditis and confirmed clinical cases according to the objective Duke criteria was assessed, and impact on estimations of disease incidence and trends.

RESULTS

In Leeds 2006-2016, 738/1681(44%) admissions containing any endocarditis code represented a definite/possible case, whilst 263/1001(24%) definite/possible endocarditis cases had no endocarditis code assigned. In Oxford 2010-2016, 307/552(56%) reviewed endocarditis-coded admissions represented a clinical case. Diagnostic codes used by most endocarditis studies had good positive predictive value (PPV) but low sensitivity (e.g. I33-primary 82% and 43% respectively); one (I38-secondary) had PPV under 6%. Estimating endocarditis incidence using raw admission data overestimated incidence trends twofold. Removing records with non-specific codes, very short stays and readmissions improved predictive ability. Estimating incidence of streptococcal endocarditis using secondary codes also overestimated increases in incidence over time. Reasons for discrepancies included changes in coding behaviour over time, and coding guidance allowing assignment of a code mentioning 'endocarditis' where endocarditis was never mentioned in the clinical notes.

CONCLUSIONS

Commonly used diagnostic codes in studies of endocarditis had good predictive ability. Other apparently plausible codes were poorly predictive. Use of diagnostic codes without examining sensitivity and predictive ability can give inaccurate estimations of incidence and trends. Similar considerations may apply to other diseases. Health record studies require validation of diagnostic codes and careful data curation to minimise risk of serious errors.

Incidence of healthcare-associated infections in a tertiary hospital in Beijing, China: results from a real-time surveillance system

Background

To quantify the five year incidence trend of all healthcare-associated infections (HAI) using a real-time HAI electronic surveillance system in a tertiary hospital in Beijing, China.

Methods

The real-time surveillance system scans the hospital’s electronic databases related to HAI (e.g. microbiological reports and antibiotics administration) to identify HAI cases. We conducted retrospective secondary analyses of the data exported from the surveillance system for inpatients with all types of HAIs from January 1st 2013 to December 31st 2017. Incidence of HAI is defined as the number of HAIs per 1000 patient-days. We modeled the incidence data using negative binomial regression.

Results

In total, 23361 HAI cases were identified from 633990 patients, spanning 6242375 patient-days during the 5-year period. Overall, the adjusted five-year HAI incidence rate had a marginal reduction from 2013 (4.10 per 1000 patient days) to 2017 (3.62 per 1000 patient days). The incidence of respiratory tract infection decreased significantly. However, the incidence rate of bloodstream infections and surgical site infection increased significantly. Respiratory tract infection (43.80%) accounted for the most substantial proportion of HAIs, followed by bloodstream infections (15.74%), and urinary tract infection (12.69%). A summer peak in HAIs was detected among adult and elderly patients.

Conclusions

This study shows how continuous electronic incidence surveillance based on existing hospital electronic databases can provide a practical means of measuring hospital-wide HAI incidence. The estimated incidence trends demonstrate the necessity for improved infection control measures related to bloodstream infections, ventilator-associated pneumonia, non-intensive care patients, and non-device-associated HAIs, especially during summer months.

Electronic supplementary material

The online version of this article (10.1186/s13756-019-0582-7) contains supplementary material, which is available to authorized users.

Incidence of Clostridioides difficile infections among young and middle-aged adults: Veterans Health Administration

Objective:

Clostridioides difficile infection (CDI) remains a significant public health concern, resulting in excess morbidity, mortality, and costs. Additional insight into the burden of CDI in adults aged <65 years is needed.

Design/Setting:

A 6-year retrospective cohort study was conducted using data extracted from United States Veterans Health Administration electronic medical records.

Patients/Methods:

Patients aged 18–64 years on January 1, 2011, were followed until incident CDI, death, loss-to-follow-up, or December 31, 2016. CDI was identified by a diagnosis code accompanied by metronidazole, vancomycin, or fidaxomicin therapy, or positive laboratory test. The clinical setting of CDI onset was defined according to 2017 SHEA-IDSA guidelines.

Results:

Of 1,073,900 patients, 10,534 had a CDI during follow-up. The overall incidence rate was 177 CDIs per 100,000 person years, rising steadily from 164 per 100,000 person years in 2011 to 189 per 100,000 person years in 2016. Those with a CDI were slightly older (55 vs 51 years) and sicker, with a higher baseline Charlson comorbidity index score (1.4 vs 0.5) than those without an infection. Nearly half (48%) of all incident CDIs were community associated, and this proportion rose from 41% in 2011 to 56% in 2016.

Conclusions:

The findings from this large retrospective study indicate that CDI incidence, driven primarily by increasing community-associated infection, is rising among young and middle-aged adult Veterans with high service-related disability. The increasing burden of community associated CDI in this vulnerable population warrants attention. Future studies quantifying the economic and societal burden of CDI will inform decisions surrounding prevention strategies.

The incremental cost of infections associated with multidrug-resistant organisms in the inpatient hospital setting-A national estimate

OBJECTIVE

To estimate the cost of infections associated with multidrug-resistant organisms (MDROs) during inpatient hospitalization in the United States.

DATA SOURCES/STUDY SETTING

2014 National Inpatient Sample.

STUDY DESIGN

Multivariable regression models assessed the incremental effect of MDROs on the cost of hospitalization and hospital length of stay among patients with bacterial infections.

DATA COLLECTION/EXTRACTION METHODS

We retrospectively identified 6 385 258 inpatient stays for patients with bacterial infection.

PRINCIPAL FINDINGS

The national incidence rate of inpatient stays with bacterial infection is 20.1 percent. At least 10.8 percent of such stays-and as many as 16.9 percent if we account for undercoded infections-show evidence of one or more MDROs. MRSA, C. difficile, infection with another MDRO, and the presence of more than one MDRO are associated with $1718 (95% CI, $1609-$1826), $4617 (95% CI, $4407-$4827), $2302 (95% CI, $2044-$2560), and $3570 (95% CI, $3019-$4122) in additional costs per stay, respectively. The national cost of infections associated with MDROs is at least $2.39 billion (95% CI, $2.25-$2.52 billion) and as high as $3.38 billion (95% CI, $3.13-$3.62 billion) if we account for undercoded infections.

CONCLUSIONS

Infections associated with MDROs result in a substantial cost burden to the US health care system.