To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Background: Previously, we reported decreasing postadmission urine-culture rates in hospitalized patients between 2012 and 2017, indicating a possible decrease in hospital-onset urinary tract infections or changes in diagnostic practices in acute-care hospitals (ACHs). In this study, we re-evaluated the trends using more recent data from 2017–2020 to assess whether new trends in hospital urine-culturing practices had emerged. Method: We conducted a longitudinal analysis of monthly urine-culture rates using microbiology data from 355 ACHs participating in the Premier Healthcare Database in 2017–2020. All cultures from the urinary tract collected on or before day 3 were defined as admission urine cultures and those collected on day 4 or later were defined as postadmission urine cultures. We included discharges from months where a hospital reported at least 1 urine culture with microbiology and antimicrobial susceptibility test results. Annual estimates of rates of admission culture and postadmission urine-culture rates were assessed using general estimating equation models with a negative binomial distribution accounting for hospital-level clustering and adjusting for hospital bed size, teaching status, urban–rural designation, discharge month, and census division. Estimated rate for each year (2018, 2019, and 2020) was compared to previous year’s estimated rate using rate ratios (RRs) and 95% confidence intervals (CIs) generated through the multivariable GEE models. Results: From 2017 to 2020, we included 8.7 million discharges and 1,943,540 urine cultures, of which 299,013 (15.4%) were postadmission urine cultures. In 2017–2020, unadjusted admission culture rates were 20.0, 19.6, 17.9, and 18.2 per 100 discharges respectively; similarly, unadjusted postadmission urine-culture rates were 8.6, 7.8, 7.0, and 7.5 per 1,000 patient days. In the multivariable analysis, adjusting for hospital characteristics, no significant changes in admission urine-culture rates were detected during 2017–2019; however, in 2020, admission urine-culture rates increased 6% compared to 2019 (RR, 1.06; 95% CI, 1.02–1.09) (Fig. 1). Postadmission urine-culture rates decreased 4% in 2018 compared to 2017 (RR, 0.96; 95% CI, 0.91–0.99) and 8% in 2019 compared to 2018 (RR, 0.92; 95% CI, 0.87–0.96). In 2020, postadmission urine-culture rates increased 10% compared to 2019 (RR, 1.10; 95% CI, 1.06–1.14) (Fig. 2). Factors significantly associated with postadmission urine-culture rates included discharge month and hospital bed size. For admission urine cultures, discharge month was the only significant factor. Conclusions: Between 2017–2019, postadmission urine-culture rates continued a decreasing trend, while admission culture rates remained unchanged. However, in 2020 both admission and postadmission urine culture rates increased significantly in comparison to 2019.
From 2014 to 2020, we compiled radiocarbon ages from the lower 48 states, creating a database of more than 100,000 archaeological, geological, and paleontological ages that will be freely available to researchers through the Canadian Archaeological Radiocarbon Database. Here, we discuss the process used to compile ages, general characteristics of the database, and lessons learned from this exercise in “big data” compilation.
Early reporting of atypical symptoms following a mild traumatic brain injury (mTBI) may be an early indicator of poor prognosis. This study aimed to determine the percentage of people reporting atypical symptoms 1-month post-mTBI and explore links to recovery 12 months later in a community-dwelling mTBI sample.
Adult participants (>16 years) who had experienced a mTBI were identified from a longitudinal incidence study (BIONIC). At 1-month post-injury, 260 participants completed the Rivermead Post-Concussion Symptoms Questionnaire (typical symptoms) plus four atypical symptom items (hemiplegia, difficulty swallowing, digestion problems and difficulties with fine motor tasks). At 12 months post-injury, 73.9% (n = 193) rated their overall recovery on a 100-point scale. An ordinal regression explored the association between atypical symptoms at 1 month and recovery at 12 months post-injury (low = 0–80, moderate = 81–99 and complete recovery = 100), whilst controlling for age, sex, rehabilitation received, ethnicity, mental and physical comorbidities and additional injuries sustained at the time of injury.
At 1-month post-injury <1% of participants reported hemiplegia, 5.4% difficulty swallowing, 10% digestion problems and 15.4% difficulties with fine motor tasks. The ordinal regression model revealed atypical symptoms were not significant predictors of self-rated recovery at 12 months. Older age at injury and higher typical symptoms at 1 month were independently associated with poorer recovery at 12 months, p < 0.01.
Atypical symptoms on initial presentation were not linked to global self-reported recovery at 12 months. Age at injury and typical symptoms are stronger early indicators of longer-term prognosis. Further research is needed to determine if atypical symptoms predict other outcomes following mTBI.
Cardiac transplantation is a life-saving procedure for children with heart failure unresponsive to medical management. Congenital heart disease remains the most common indication for recipients under 1 year of age. Dilated cardiomyopathy, the most common etiology for transplantation in the older child, is increasingly a reason for heart transplantation in patients <1 year of age. Complications of cardiac transplantation in the early postoperative period include acute rejection, anastomotic related issues with the transplanted heart, and postoperative infection. Chronic complications include rejection, infection, and cardiac allograft vasculopathy. Children with heart failure do not always appear ill despite compromised cardiac function, and this chapter aids in the perioperative assessment and management of a patient with a failing heart transplant.
Testosterone (T) and cortisol (C) are the end products of neuroendocrine axes that interact with the process of shaping brain structure and function. Relative levels of T:C (TC ratio) may alter prefrontal–amygdala functional connectivity in adulthood. What remains unclear is whether TC-related effects are rooted to childhood and adolescence. We used a healthy cohort of 4–22-year-olds to test for associations between TC ratios, brain structure (amygdala volume, cortical thickness (CTh), and their coordinated growth), as well as cognitive and behavioral development. We found greater TC ratios to be associated with the growth of specific brain structures: 1) parietal CTh; 2) covariance of the amygdala with CTh in visual and somatosensory areas. These brain parameters were in turn associated with lower verbal/executive function and higher spatial working memory. In sum, individual TC profiles may confer a particular brain phenotype and set of cognitive strengths and vulnerabilities, prior to adulthood.
Previously reported associations between hospital-level antibiotic use and hospital-onset Clostridioides difficile infection (HO-CDI) were reexamined using 2012–2018 data from a new cohort of US acute-care hospitals. This analysis revealed significant positive associations between total, third-generation, and fourth-generation cephalosporin, fluoroquinolone, carbapenem, and piperacillin-tazobactam use and HO-CDI rates, confirming previous findings.
Countries worldwide are experiencing a third wave of the coronavirus disease 2019 (COVID-19) pandemic. Government-imposed restrictive measures continue with undetermined effects on physical and mental health.
To compare child and adolescent mental health services (CAMHS) referrals over 11 months (January–November) in 2020, 2019 and 2018 and examine any impact the different phases of the COVID-19 restrictions might have on referral rates.
Monthly CAMHS Health Service Executive data were examined, covering a catchment population of 260 560 or 12.7% of all youth (age group 0–18 years) in Ireland. The total number of urgent and routine referrals, appointments offered, rates of non-attendances and discharge outcome are presented.
There was a significant drop in referrals in 2020, compared with prior years (χ2 = 10.3, d.f. = 2, P = 0.006). Referrals in 2020 dropped from March to May by 11% and from June to August by 10.3%. From September, both routine and urgent referrals increased by 50% compared with previous years (2018/2019), with the highest increase in November 2020 (180%). Clinic activity also increased from September, with double the number of out-patient appointments offered, compared with previous years (χ2 = 5171.72, d.f. = 3, P < 0.001) and lower (6.6%) rates of non-attendance (χ2 = 868.35, d.f. = 3, P < 0.001).
In 2020, following an initial decline, referrals to CAMHS increased consistently from September. Such unprecedented increase in referrals places further strain on services that are already underresourced and underfunded, with the likelihood of increased waiting lists post COVID-19. It is envisaged that once the pandemic is over, resources will be even more constrained, and CAMHS will be urgently in need of additional ring-fenced funding.
This case report shares the story of a family who sought care elsewhere after their daughter was denied cardiac surgery in their home state because she had trisomy 18. This case report recommends case-by-case assessment of cardiac surgical interventions for children with trisomy 13 or 18 as informed by review of goals, assessment of comorbidities, and literature-informed practice. Coordinated care planning and interdisciplinary communication are relevant in cardiac surgical considerations for children with these underlying genetic conditions.
Nutrition during the periconceptional period influences postnatal cardiovascular health. We determined whether in vitro embryo culture and transfer, which are manipulations of the nutritional environment during the periconceptional period, dysregulate postnatal blood pressure and blood pressure regulatory mechanisms. Embryos were either transferred to an intermediate recipient ewe (ET) or cultured in vitro in the absence (IVC) or presence of human serum (IVCHS) and a methyl donor (IVCHS+M) for 6 days. Basal blood pressure was recorded at 19–20 weeks after birth. Mean arterial pressure (MAP) and heart rate (HR) were measured before and after varying doses of phenylephrine (PE). mRNA expression of signaling molecules involved in blood pressure regulation was measured in the renal artery. Basal MAP did not differ between groups. Baroreflex sensitivity, set point, and upper plateau were also maintained in all groups after PE stimulation. Adrenergic receptors alpha-1A (αAR1A), alpha-1B (αAR1B), and angiotensin II receptor type 1 (AT1R) mRNA expression were not different from controls in the renal artery. These results suggest there is no programmed effect of ET or IVC on basal blood pressure or the baroreflex control mechanisms in adolescence, but future studies are required to determine the impact of ET and IVC on these mechanisms later in the life course when developmental programming effects may be unmasked by age.
Background: Clinically diagnosed ventilator-associated pneumonia (VAP) is common in the long-term acute-care hospital (LTACH) setting and may contribute to adverse ventilator-associated events (VAEs). Pseudomonas aeruginosa is a common causative organism of VAP. We evaluated the impact of respiratory P. aeruginosa colonization and bacterial community dominance, both diagnosed and undiagnosed, on subsequent P. aeruginosa VAP and VAE events during long-term acute care. Methods: We enrolled 83 patients on LTACH admission for ventilator weaning, performed longitudinal sampling of endotracheal aspirates followed by 16S rRNA gene sequencing (Illumina HiSeq), and bacterial community profiling (QIIME2). Statistical analysis was performed with R and Stan; mixed-effects models were fit to relate the abundance of respiratory Psa on admission to clinically diagnosed VAP and VAE events. Results: Of the 83 patients included, 12 were diagnosed with P. aeruginosa pneumonia during the 14 days prior to LTACH admission (known P. aeruginosa), and 22 additional patients received anti–P. aeruginosa antibiotics within 48 hours of admission (suspected P. aeruginosa); 49 patients had no known or suspected P. aeruginosa (unknown P. aeruginosa). Among the known P. aeruginosa group, all 12 patients had P. aeruginosa detectable by 16S sequencing, with elevated admission P. aeruginosa proportional abundance (median, 0.97; IQR, 0.33–1). Among the suspected P. aeruginosa group, all 22 patients had P. aeruginosa detectable by 16S sequencing, with a wide range of admission P. aeruginosa proportional abundance (median, 0.0088; IQR, 0.00012–0.31). Of the 49 patients in the unknown group, 47 also had detectable respiratory Psa, and many had high P. aeruginosa proportional abundance at admission (median, 0.014; IQR, 0.00025–0.52). Incident P. aeruginosa VAP was observed within 30 days in 4 of the known P. aeruginosa patients (33.3%), 5 of the suspected P. aeruginosa patients (22.7%), and 8 of the unknown P. aeruginosa patients (16.3%). VAE was observed within 30 days in 1 of the known P. aeruginosa patients (8.3%), 2 of the suspected P. aeruginosa patients (9.1%), and 1 of the unknown P. aeruginosa patients (2%). Admission P. aeruginosa abundance was positively associated with VAP and VAE risk in all groups, but the association only achieved statistical significance in the unknown group (type S error <0.002 for 30-day VAP and <0.011 for 30-day VAE). Conclusions: We identified a high prevalence of unrecognized respiratory P. aeruginosa colonization among patients admitted to LTACH for weaning from mechanical ventilation. The admission P. aeruginosa proportional abundance was strongly associated with increased risk of incident P. aeruginosa VAP among these patients.
Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is an important cause of healthcare-associated infections with limited treatment options and high mortality. To describe risk factors for mortality, we evaluated characteristics associated with 30-day mortality in patients with CRAB identified through the Emerging Infections Program (EIP). Methods: From January 2012 through December 2017, 8 EIP sites (CO, GA, MD, MN, NM, NY, OR, TN) participated in active, laboratory-, and population-based surveillance for CRAB. An incident case was defined as patient’s first isolation in a 30-day period of A. baumannii complex from sterile sites or urine with resistance to ≥1 carbapenem (excluding ertapenem). Medical records were abstracted. Patients were matched to state vital records to assess mortality within 30 days of incident culture collection. We developed 2 multivariable logistic regression models (1 for sterile site cases and 1 for urine cases) to evaluate characteristics associated with 30-day mortality. Results: We identified 744 patients contributing 863 cases, of which 185 of 863 cases (21.4%) died within 30 days of culture, including 113 of 257 cases (44.0%) isolated from a sterile site and 72 of 606 cases (11.9%) isolated from urine. Among 628 hospitalized cases, death occurred in 159 cases (25.3%). Among hospitalized fatal cases, death occurred after hospital discharge in 27 of 57 urine cases (47.4%) and 21 of 102 cases from sterile sites (20.6%). Among sterile site cases, female sex, intensive care unit (ICU) stay after culture, location in a healthcare facility, including a long-term care facility (LTCF), 3 days before culture, and diagnosis of septic shock were associated with increased odds of death in the model (Fig. 1). In urine cases, age 40–54 or ≥75 years, ICU stay after culture, presence of an indwelling device other than a urinary catheter or central line (eg, endotracheal tube), location in a LTCF 3 days before culture, diagnosis of septic shock, and Charlson comorbidity score ≥3 were associated with increased odds of mortality (Fig. 2). Conclusion: Overall 30-day mortality was high among patients with CRAB, including patients with CRAB isolated from urine. A substantial fraction of mortality occurred after discharge, especially among patients with urine cases. Although there were some differences in characteristics associated with mortality in patients with CRAB isolated from sterile sites versus urine, LTCF exposure and severe illness were associated with mortality in both patient groups. CRAB was associated with major mortality in these patients with evidence of healthcare experience and complex illness. More work is needed to determine whether prevention of CRAB infections would improve outcomes.
Background: Studies on the effectiveness of hospital-based interventions often measure hospital-onset infections as the outcome of interest. However, hospital-associated infections may manifest after patient discharge (classified as hospital-associated community-onset, HACO), and the epidemiology may vary by antibiotic resistance (AR) profile. We examined the epidemiology and trends of HACO infections of AR and non–antibiotic-resistant (non-AR) bacteria. Methods: We included clinical community-onset (CO) cultures (obtained sooner than or on day 3 of hospitalization) yielding the bacterial species of interest among hospitalized patients in 260 hospitals in the Premier Healthcare Database from 2012 to 2017. HACO infections were defined as CO cultures in a patient who had a previous hospitalization in the same hospital within 30 days. We examined methicillin resistance among Staphylococcus aureus (MRSA), vancomycin resistance among Enterococcus spp (VRE), carbapenem resistance among Enterobacteriaceae (E. coli, Klebsiella spp, and Enterobacter spp) (CRE), extended-spectrum cephalosporin resistance suggestive of extended-spectrum β-lactamase (ESBL) production in Enterobacteriaceae, carbapenem resistance among Acinetobacter spp (CRAsp), and carbapenem resistance among Pseudomonas aeruginosa (CRPA). We described the proportion of CO infections that were HACO, the proportion of HACO infections from sterile sites, overall HACO rates, and annual trends for sensitive and resistant phenotypes. Generalized estimating equation regression models that accounted for hospital-level clustering were used to estimate annual trends controlling for hospital characteristics and month of discharge. Results: The rate of HACO infections by pathogen ranged from 0.78 to 38.76 per 10,000 hospitalizations; 7%–34% were sterile site infections (Table 1). For each bacterial pathogen, a significantly higher proportion of AR CO infections had a previous hospitalization compared to non-AR CO infections (all χ2, P < .05). The annual trends for AR and non-AR HACO infections between 2012 and 2017 were significantly decreasing for most pathogens, except ESBL HACO infections. Conclusions: Even when using a definition limited to readmission to the same hospital, HACO infections occur commonly with differing rates by pathogen and antibiotic resistance profile. Although these rates are decreasing for most of the pathogens studied, improving surveillance and identifying prevention strategies for these infections are necessary to further reduce the burden of hospital-associated infections.
Background: In recent years, the historic declines in the incidence of methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) in the United States have slowed. We examined trends in the incidence of community-onset (CO) MRSA BSIs among hospitalized persons with and without substance-use diagnoses. Methods: Using data from >200 US hospitals reporting to the Premier Healthcare Database (PHD) during 2012–2017, we conducted a retrospective study among hospitalized persons aged ≥18 years. MRSA BSIs with substance use were defined as hospitalizations having both a blood culture positive for MRSA and at least 1 International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) or ICD-10-CM diagnostic code for substance use including opioids, cocaine, amphetamines, or other substances (excluding cannabis, alcohol, and nicotine). MRSA BSIs were considered community onset when a positive blood culture was collected within 3 days of admission. We assessed annual trends and described characteristics of CO MRSA BSI hospitalizations, stratified by substance use. Results: Of 20,049 MRSA BSIs from 2012 to 2017, 17,634 (88%) were CO. Overall, MRSA BSI incidence decreased 7%, from 178.5 to 166.2 per 100,000 hospitalizations during the study period; However, CO MRSA BSI rates remained stable (152.7 to 149.9 per 100,000 hospitalizations). Among CO MRSA BSIs, 1,838 (10%) were BSIs with substance-use diagnoses; the incidence of CO MRSA BSIs with substance use increased 236% (from 8.2 to 27.6 per 100,000 hospitalizations) and represented a greater proportion of the CO MRSA rate over the study period (Fig. 1). The incidence of CO MRSA BSIs without substance use decreased 15% (from 144.5 to 122.4 per 100,000 hospitalizations). Patients with CO MRSA BSIs with substance use were younger (median, 40 vs 65 years), more likely to be female (50% vs 40%), white (79% vs 69%), and to leave against medical advice (15% vs 1%). Among patients not leaving against medical advice, CO BSI patients with substance-use diagnoses had longer lengths of stay (median, 11 vs 9 days), lower in-hospital mortality (9% vs 14%), and higher hospitalization costs (median, $22,912 vs $17,468) compared to patients without substance-use diagnoses. Conclusions: Although the overall CO MRSA BSI rate remained unchanged from 2012 to 2017, infections with substance use diagnoses increased >3-fold, and infections without substance use diagnoses decreased. These data suggest that the emergence of MRSA associated with substance-use diagnoses threatens potential progress in reducing the incidence of CO MRSA infections. Additional strategies may be needed to prevent MRSA BSI in patients with substance-use diagnoses, and to maintain national progress in the reduction of MRSA infections overall.
Background: Epidemiological studies have utilized administrative discharge diagnosis codes to identify methicillin-resistant and methicillin-sensitive Staphylococcus aureus (MRSA and MSSA) infections and trends, despite debate regarding the accuracy of utilizing codes for this purpose. We assessed the sensitivity and positive predictive value (PPV) of MRSA- and MSSA-specific diagnosis codes, trends, characteristics, and outcomes of S. aureus hospitalizations by method of identification. Methods: Clinical micro biology results and discharge data from geographically diverse US hospitals participating in the Premier Healthcare Database from 2012–2017 were used to identify monthly rates of MRSA and MSSA. Positive MRSA or MSSA clinical cultures and/or a MRSA- or MSSA-specific International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10 CM) diagnosis codes from adult inpatients (aged ≥18 years) were included as S. aureus hospitalizations. Septicemia was defined as a positive blood culture or a MRSA or MSSA septicemia code. Sensitivity and PPV for codes were calculated for hospitalizations where admission status was not listed as transfer; true infection was considered a positive clinical culture. Negative binominal regression models measured trends in rates of MRSA and MSSA per 1,000 hospital discharges. Results: We identified 168,634 MRSA and 148,776 MSSA hospitalizations in 256 hospitals; 17% of MRSA and 21% of MSSA were septicemia. Less than half of all S. aureus hospitalizations (49% MRSA, 46% MSSA) and S. aureus septicemia hospitalizations (37% MRSA, 38% MSSA) had both a positive culture and diagnosis code (Fig. 1). Sensitivity of MRSA codes in identifying positive cultures was 61% overall and 56% for septicemia, PPV was 62% overall and 53% for septicemia. MSSA codes had a sensitivity of 49% in identifying MSSA cultures and 52% for MSSA septicemia; PPV was 69% overall and 62% for septicemia. Despite low sensitivity, MRSA trends are similar for cultures and codes, and MSSA trends are divergent (Fig. 2). For hospitalizations with septicemia, mortality was highest among those with a blood culture only (31.3%) compared to hospitalizations with both a septicemia code and blood culture (16.6%), and septicemia code only (14.7%). Conclusions: ICD diagnosis code sensitivity and PPV for identifying infections were consistently poor in recent years. Less than half of hospitalizations have concordant microbiology laboratory results and diagnosis codes. Rates and trend estimates for MSSA differ by method of identification. Using diagnosis codes to identify S. aureus infections may not be appropriate for descriptive epidemiology or assessing trends due to significant misclassification.
Disclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.
Background: Microbiology data are utilized to quantify epidemiology and trends in pathogens, antimicrobial resistance, and bloodstream infections. Understanding variability and trends in rates of hospital-level blood culture utilization may be important for interpreting these findings. Methods: We used clinical microbiology results and discharge data to identify monthly blood culture rates from US hospitals participating in the Premier Healthcare Database during 2012–2017. We included all discharges from months where a hospital reported at least 1 blood culture with microbiology and antimicrobial susceptibility results. Blood cultures drawn on or before day 3 were defined as admission cultures (ACs); blood cultures collected after day 3 were defined as a postadmission cultures (PACs). The AC rate was defined as the proportion of all hospitalizations with an AC. The PAC rate was defined as the number of days with a PAC among all patient days. Generalized estimating equation regression models that accounted for hospital-level clustering with an exchangeable correlation matrix were used to measure associations of monthly rates with hospital bed size, teaching status, urban–rural designation, region, month, and year. The AC rates were modeled using logistic regression, and the PAC rates were modeled using a Poisson distribution. Results: We included 11.7 million hospitalizations from 259 hospitals, accounting for nearly 52 million patient days. The median annual hospital-level AC rate was 27.1%, with interhospital variation ranging from 21.1% (quartile 1) to 35.2% (quartile 3) (Fig. 1). Multivariable models revealed no significant trends over time (P = .74), but statistically significant associations between AC rates with month (P < .001) and region (P = .003), associations with teaching status (P = .063), and urban-rural designation (P = .083) approached statistical significance. There was no association with bed size (P = .38). The median annual hospital-level PAC rate was 11.1 per 1,000 patient days, and interhospital variability ranged from 7.6 (quartile 1) to 15.2 (quartile 3) (Fig. 2). Multivariable models of PAC rates showed no significant trends over time (P = .12). We found associations between PAC rates with month (P = .016), bed size (P = .030), and teaching status (P = .040). PAC rates were not associated with urban–rural designation (P = .52) or region (P = .29). Conclusions: Blood culture utilization rates in this large cohort of hospitals were unchanged between 2012 and 2017, though substantial interhospital variability was detected. Although both AC and PAC rates vary by time of year and potentially by teaching status, AC rates vary by geographic characteristics whereas PAC rates vary by bed size. These factors are important to consider when comparing rates of bloodstream infections by hospital.
Background: Healthcare exposure results in significant microbiome disruption, particularly in the setting of critical illness, which may contribute to risk for healthcare-associated infections (HAIs). Patients admitted to long-term acute-care hospitals (LTACHs) have extensive prior healthcare exposure and critical illness; significant microbiome disruption has been previously documented among LTACH patients. We compared the predictive value of 3 respiratory tract microbiome disruption indices—bacterial community diversity, dominance, and absolute abundance—as they relate to risk for ventilator-associated pneumonia (VAP) and adverse ventilator-associated events (VAE), which commonly complicate LTACH care. Methods: We enrolled 83 subjects on admission to an academic LTACH for ventilator weaning and performed longitudinal sampling of endotracheal aspirates, followed by 16S rRNA gene sequencing (Illumina HiSeq), bacterial community profiling (QIIME2) for diversity, and 16S rRNA quantitative PCR (qPCR) for total bacterial abundance. Statistical analyses were performed with R and Stan software. Mixed-effects models were fit to relate the admission MDIs to subsequent clinically diagnosed VAP and VAE. Results: Of the 83 patients, 19 had been diagnosed with pneumonia during the 14 days prior to LTACH admission (ie, “recent past VAP”); 23 additional patients were receiving antibiotics consistent with empiric VAP therapy within 48 hours of admission (ie, “empiric VAP therapy”); and 41 patients had no evidence of VAP at admission (ie, “no suspected VAP”). We detected no statistically significant differences in admission Shannon diversity, maximum amplicon sequence variant (ASV)–level proportional abundance, or 16S qPCR across the variables of interest. In isolation, all 3 admission microbiome disruption indices showed poor predictive performance, though Shannon diversity performed better than maximum ASV abundance. Predictive models that combined (1) bacterial diversity or abundance with (2) recent prior VAP diagnosis and (3) concurrent antibiotic exposure best predicted 14-day VAP (type S error < 0.05) and 30-day VAP (type S error < 0.003). In this cohort, VAE risk was paradoxically associated with higher admission Shannon diversity and lower admission maximum ASV abundance. Conclusions: In isolation, respiratory tract microbiome disruption indices obtained at LTACH admission showed poor predictive performance for subsequent VAP and VAE. But diversity and abundance models incorporating recent VAP history and admission antibiotic exposure performed well predicting 14-day and 30-day VAP.