Sepsis surveillance from administrative data in the absence of a perfect verification

Misclassification Error-Adjusted Prevalence of Injection Drug Use Among Infective Endocarditis Hospitalizations in the United States: A Serial Cross-Sectional Analysis of the 2007-2016 National Inpatient Sample

Administrative health databases have been used to monitor trends in infective endocarditis hospitalization related to nonprescription injection drug use (IDU) using International Classification of Diseases (ICD) code algorithms. Because no ICD code for IDU exists, drug dependence and hepatitis C virus (HCV) have been used as surrogate measures for IDU, making misclassification error (ME) a threat to the accuracy of existing estimates. In a serial cross-sectional analysis, we compared the unadjusted and ME-adjusted prevalences of IDU among 70,899 unweighted endocarditis hospitalizations in the 2007-2016 National Inpatient Sample. The unadjusted prevalence of IDU was estimated with a drug algorithm, an HCV algorithm, and a combination algorithm (drug and HCV). Bayesian latent class models were used to estimate the median IDU prevalence and 95% Bayesian credible intervals and ICD algorithm sensitivity and specificity. Sex- and age group-stratified IDU prevalences were also estimated. Compared with the misclassification-adjusted prevalence, unadjusted estimates were lower using the drug algorithm and higher using the combination algorithm. The median ME-adjusted IDU prevalence increased from 9.7% (95% Bayesian credible interval (BCI): 6.3, 14.8) in 2008 to 32.5% (95% BCI: 26.5, 38.2) in 2016. Among persons aged 18-34 years, IDU prevalence was higher in females than in males. ME adjustment in ICD-based studies of injection-related endocarditis is recommended.

Validity of ICD-based algorithms to estimate the prevalence of injection drug use among infective endocarditis hospitalizations in the absence of a reference standard

BACKGROUND

International Classification of Diseases (ICD) code algorithms are routinely used to estimate the frequency of illicit injection drug use (IDU)-associated hospitalizations in administrative health datasets despite a lack of evidence regarding their validity. We aimed to measure the sensitivity and specificity of ICD code algorithms used to estimate the prevalence of current/recent IDU among infective endocarditis (IE) hospitalizations without a reference standard.

METHODS

We reviewed medical records of 321 patients aged 18-64 years old from an urban academic hospital with an IE diagnosis between 2007 and 2017. Diagnostic tests for IDU included self-reported IDU in medical records; a drug use, abuse and dependence (UAD) ICD algorithm; a Hepatitis C Virus (HCV) ICD algorithm; and a combination drug UAD/HCV ICD algorithm. Sensitivity, specificity and the misclassification error (ME)-adjusted IDU prevalence were estimated using Bayesian latent class models.

RESULTS

The combination algorithm had the highest sensitivity and lowest specificity. Sensitivity increased for the drug UAD algorithm in the ICD-10 period compared to the ICD-9 period. The ME-adjusted current/recent IDU prevalence estimated using the drug UAD and HCV algorithms was 23 % (95 % Bayesian credible interval: 16 %, 31 %). The unadjusted prevalence estimate from the drug UAD algorithm underestimated the ME-adjusted prevalence, while the combination algorithm overestimated it.

CONCLUSION

The validity of ICD code algorithms for IDU among IE hospitalizations is imperfect and differs between ICD-9 and ICD-10. Commonly used ICD-based algorithms could lead to substantially biased prevalence estimates in IDU-associated hospitalizations when using administrative health data.

Trends in Hospitalizations for Serious Infections in Patients With Rheumatoid Arthritis in the US Between 1993 and 2013

OBJECTIVE

The epidemiology of hospitalizations with infections among patients with rheumatoid arthritis (RA) is unknown, despite an increase in RA treatments that confer a risk of infection.

METHODS

We examined National Inpatient Sample data from 1993-2013. We identified hospitalizations among adults with RA, defined by International Classification of Diseases, Ninth Revision, Clinical Modification codes (714.xx) in any secondary diagnosis field. We evaluated 5 infections as the primary diagnosis: pneumonia, sepsis, urinary tract infection (UTI), skin and soft tissue infections (SSTIs), and opportunistic infections (OIs). The primary outcome was the proportion of hospitalizations for each infection among all hospitalizations with a secondary diagnosis of RA.

RESULTS

There were 792,921 hospitalizations for infection with a secondary diagnosis of RA, with the rates increasing from 90 to 206 per 100,000 persons from 1993-2013. The proportion of hospitalizations decreased for pneumonia (from 5.4% to 4.6%), UTI (from 0.4% to 0.38%), and OIs (from 0.44% to 0.26%). The proportion of hospitalizations for SSTIs increased slightly (from 2.3% to 2.5%), while hospitalizations for sepsis more than tripled (from 1.9% to 6.4%).

CONCLUSION

Between 1993 and 2013, the proportion of hospitalizations for infections among RA patients appeared to decline for pneumonia and OIs, with a slight decrease in UTI, a slight increase in SSTIs, and a substantial increase in hospitalizations with sepsis. Our results are consistent with previous reports that the sensitivity of sepsis coding has increased over time.

Updated Estimates Suggest a Much Higher Prevalence of Arthritis in United States Adults Than Previous Ones

OBJECTIVE

National estimates of arthritis prevalence rely on a single survey question about doctor-diagnosed arthritis without using survey information on joint symptoms, even though some subjects with only the latter have been shown to have arthritis. The sensitivity of the current surveillance definition is only 53% and 69% in subjects ages 45-64 years and ages ≥65 years, respectively, resulting in misclassification of nearly one-half and one-third of subjects in those age groups. This study was undertaken to estimate arthritis prevalence based on an expansive surveillance definition that is adjusted for the measurement errors in the current definition.

METHODS

Using the 2015 National Health Interview Survey, we developed a Bayesian multinomial latent class model for arthritis surveillance based on doctor-diagnosed arthritis, joint symptoms, and whether symptom duration exceeded 3 months.

RESULTS

Of 33,672 participants, 19.3% of men and 16.7% of women ages 18-64 years and 15.7% of men and 13.5% of women ages ≥65 years affirmed joint symptoms without doctor-diagnosed arthritis. The measurement error-adjusted prevalence of arthritis was 29.9% (95% Bayesian probability interval [95% PI] 23.4-42.3) in men ages 18-64 years, 31.2% (95% PI 25.8-44.1) in women ages 18-64 years, 55.8% (95% PI 49.9-70.4) in men ages ≥65 years, and 68.7% (95% PI 62.1-79.9) in women ages ≥65 years. Arthritis affected 91.2 million adults (of 247.7 million; 36.8%) in the US in 2015, which included 61.1 million persons between 18 and 64 years of age (of 199.9 million; 30.6%). Our arthritis prevalence estimate was 68% higher than the previously reported national estimate.

CONCLUSION

Arthritis prevalence in the US population has been substantially underestimated, especially among adults younger than 65 years of age.

Dual Functioned Pegylated Phospholipid Micelles Containing Cationic Antimicrobial Decapeptide for Treating Sepsis

Despite intensive investigation of molecular mechanisms underlying the pathogenesis of sepsis, many aspects of sepsis remain unresolved; this hampers the development of appropriate therapeutics. In the present study, we developed a biologic nanomedicine containing a cationic antimicrobial decapeptide KSLW (KKVVFWVKFK), self-associated with biocompatible and biodegradable PEGylated phospholipid micelles (PLM), and analyzed its efficacy for treating sepsis. KSLW was modified with polyethylene glycol (PEG)-aldehyde or was conjugated with distearoylphosphatidylethanolamine (DSPE) -PEG-aldehyde. We compared the antibacterial and antiseptic effects of PEG-KSLW and PLM-KSLW with those of unmodified KSLW both in vitro and in vivo. We found that the PLM-KSLW improved the survival rate of sepsis mouse models without undesired immune responses, and inhibited lipopolysaccharide (LPS)-induced severe vascular inflammatory responses in human umbilical vein endothelial cells compared with unmodified KSLW or PEG-KSLW. Furthermore, PLM-KSLW dramatically reduced the bacterial count and inhibited bacterial growth. We also found a new role of PLM-KSLW in tightening vascular barrier integrity by binding to the glycine/tyrosine-rich domain of occludin (OCLN). Our results showed that PLM-KSLW had a more effective antiseptic effect than KSLW or PEG-KSLW, possibly because of its high affinity toward OCLN. Moreover, PLM-KSLW could be potentially used to treat severe vascular inflammatory diseases, including sepsis and septic shock.