Research on complications with peripherally inserted central catheter (PICC) lines that are placed for the treatment of prosthetic joint infection (PJI) after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is scarce. We investigated the timing, frequency, and risk factors for PICC complications during treatment of PJI after THA and TKA.

We retrospectively queried an institutional database for THA and TKA patients from January 2015 through December 2020 that developed a PJI and required PICC placement at an academic, tertiary-care referral center.

The study included 889 patients (48.3% female) with a mean age of 64.6 years (range, 18.7–95.2) who underwent 435 THAs and 454 TKAs that were revised for PJI. The cohort had 275 90-day ED visits (30.9%), and 51 (18.5%) were PICC related. The average time from discharge to PICC ED visit was 26.2 days (range, 0.3–89.4). The most common reasons for a 90-day ED visit were issues related to the joint replacement or wound site (musculoskeletal or MSK; n = 116, 42.2%) and PICC complaints (n = 51, 18.5%). A multivariable logistic regression demonstrated that non-White race (odds ratio [OR], 2.24; 95% confidence interval [CI], 1.24–4.04; P = .007) and younger age (OR, 0.98; 95% CI, 0.95–1.00; P = .035) were associated with PICC-related ED visits. Malposition/readjustment (41.2%) and occlusion (35.3%) were the most common PICC complications leading to ED presentation.

PICC complications are common after PJI treatment, accounting for nearly 20% of 90-day ED visits.

Prompt diagnosis and intervention for ventilator-associated pneumonia (VAP) is critical but can lead to overdiagnosis and overtreatment.

We investigated healthcare provider (HCP) perceptions and challenges associated with VAP diagnosis, and we sought to identify opportunities for diagnostic stewardship.

We conducted a qualitative study of 30 HCPs at a tertiary-care hospital. Participants included attending physicians, residents and fellows (trainees), advanced practice providers (APPs), and pharmacists. Interviews were composed of open-ended questions in 4 sections: (1) clinical suspicion and thresholds for respiratory culture ordering, (2) preferences for respiratory sample collection, (3) culture report interpretation, and (4) VAP diagnosis and treatment. Interviews transcripts were analyzed using Nvivo 12 software, and responses were organized into themes.

Overall, 10 attending physicians (75%) and 16 trainees (75%) trainees and APPs believed they were overdiagnosing VAP; this response was frequent among HCPs in practice 5–10 years (91%, n = 12). Increased identification of bacteria as a result of frequent respiratory culturing, misinterpretation of culture data, and fear of missing diagnosis were recognized as drivers of overdiagnosis and overtreatment. Although most HCPs rely on clinical and radiographic changes to initiate work-up, the fear of missing a diagnosis leads to sending cultures even in the absence of those changes.

HCPs believe that VAP overdiagnosis and overtreatment are common due to fear of missing diagnosis, overculturing, and difficulty distinguishing colonization from infection. Although we identified opportunities for diagnostic stewardship, interventions influencing the ordering of cultures and starting antimicrobials will need to account for strongly held beliefs and ICU practices.

We sought to assess the presence and reporting quality of peer-reviewed literature concerning the accuracy, precision, and reliability of home monitoring technologies for vital signs and glucose determinations in older adult populations.

A narrative literature review was undertaken searching the databases Medline, Embase, and Compendex. Peer-reviewed publications with keywords related to vital signs, monitoring devices and technologies, independent living, and older adults were searched. Publications between the years 2012 and 2018 were included. Two reviewers independently conducted title and abstract screening, and four reviewers independently undertook full-text screening and data extraction with all disagreements resolved through discussion and consensus.

Two hundred nine articles were included. Our review showed limited assessment and low-quality reporting of evidence concerning the accuracy, precision, and reliability of home monitoring technologies. Of 209 articles describing a relevant device, only 45 percent (n = 95) provided a citation or some evidence to support their validation claim. Of forty-eight articles that described the use of a comparator device, 65 percent (n = 31) used low-quality statistical methods, 23 percent (n = 11) used moderate-quality statistical methods, and only 12 percent (n = 6) used high-quality statistical methods.

Our review found that current validity claims were based on low-quality assessments that do not provide the necessary confidence needed by clinicians for medical decision-making purposes. This narrative review highlights the need for standardized health technology reporting to increase health practitioner confidence in these devices, support the appropriate adoption of such devices within the healthcare system, and improve health outcomes.

To determine whether patients using the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website (http://medicare.gov/hospitalcompare) can use nationally reported healthcare-associated infection (HAI) data to differentiate hospitals.

Secondary analysis of publicly available HAI data for calendar year 2013.

We assessed the availability of HAI data for geographically proximate hospitals (ie, hospitals within the same referral region) and then analyzed these data to determine whether they are useful to differentiate hospitals. We assessed data for the 6 HAIs reported by hospitals to the Centers for Disease Control and Prevention (CDC).

Data were analyzed for 4,561 hospitals representing 88% of registered community and federal government hospitals in the United States. Healthcare-associated infection data are only useful for comparing hospitals if they are available for multiple hospitals within a geographic region. We found that data availability differed by HAI. Clostridium difficile infections (CDI) data were most available, with 82% of geographic regions (ie, hospital referral regions) having >50% of hospitals reporting them. In contrast, 4% of geographic regions had >50% of member hospitals reporting surgical site infections (SSI) for hysterectomies, which had the lowest availability. The ability of HAI data to differentiate hospitals differed by HAI: 72% of hospital referral regions had at least 1 pair of hospitals with statistically different risk-adjusted CDI rates (SIRs), compared to 9% for SSI (hysterectomy).

HAI data generally are reported by enough hospitals to meet minimal criteria for useful comparisons in many geographic locations, though this varies by type of HAI. CDI and catheter-associated urinary tract infection (CAUTI) are more likely to differentiate hospitals than the other publicly reported HAIs.

Infect Control Hosp Epidemiol 2017;38:1167–1171

Hospital-acquired infection (HAI) data are reported to the public on the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website. We previously found that public understanding of these data is poor. Our objective was to develop an improved method for presenting HAI data that could be used on the CMS website.

Randomized controlled trial comparing understanding of data presented using the current CMS presentation strategy versus a new strategy.

A 760-bed tertiary referral hospital.

A total of 61 patients were randomly selected within 24 hours of admission.

Participants were shown HAI data as presented on the CMS Hospital Compare website (control arm) or data formatted using a new method (experimental arm).

No statistically significant demographic differences were identified between study arms. Although 47% percent of participants said a website for comparing hospitals would have been helpful, only 10% had ever used such a website. Participants viewing data using the new presentation strategy compared hospitals correctly 56% of the time, compared with 32% in the control arm (P=.0002).

Understanding of HAI data increased significantly with the new data presentation method compared to the method currently used on the CMS Hospital Compare website. Many participants expressed interest in a website for comparing hospitals. Improved methods for presenting CMS HAI data, such as the one assessed here, should be adopted to increase public understanding.

Infect Control Hosp Epidemiol 2016;1–6

Public reporting of hospital quality data is a key element of US healthcare reform. Data for hospital-acquired infections (HAIs) are especially complex.

To assess interpretability of HAI data as presented on the Centers for Medicare and Medicaid Services Hospital Compare website among patients who might benefit from access to these data.

We randomly selected inpatients at a large tertiary referral hospital from June to September 2014. Participants performed 4 distinct tasks comparing hypothetical HAI data for 2 hospitals, and the accuracy of their comparisons was assessed. Data were presented using the same tabular formats used by Centers for Medicare and Medicaid Services. Demographic characteristics and healthcare experience data were also collected.

Participants (N=110) correctly identified the better of 2 hospitals when given written descriptions of the HAI measure in 72% of the responses (95% CI, 66%–79%). Adding the underlying numerical data (number of infections, patient-time, and standardized infection ratio) to the written descriptions reduced correct responses to 60% (55%–66%). When the written HAI measure description was not informative (identical for both hospitals), 50% answered correctly (42%–58%). When no written HAI measure description was provided and hospitals differed by denominator for infection rate, 38% answered correctly (31%–45%).

Current public HAI data presentation methods may be inadequate. When presented with numeric HAI data, study participants incorrectly compared hospitals on the basis of HAI data in more than 40% of the responses. Research is needed to identify better ways to convey these data to the public.

Infect. Control Hosp. Epidemiol. 2016;37(2):182–187