Abstract
Background
Febrile neutropenia (FN) is a common complication to chemotherapy associated with a high burden of morbidity and mortality. Reliable prediction of individual risk based on ...pretreatment risk factors allows for stratification of preventive interventions. We aimed to develop such a risk stratification model to predict FN in the 30 days after initiation of chemotherapy.
Methods
We included consecutive treatment-naïve patients with solid cancers and diffuse large B-cell lymphomas at Copenhagen University Hospital, 2010–2015. Data were obtained from the PERSIMUNE repository of electronic health records. FN was defined as neutrophils ≤0.5 × 10E9/L at the time of either a blood culture sample or death. Time from initiation of chemotherapy to FN was analyzed using Fine-Gray models with death as a competing event. Risk factors investigated were: age, sex, body surface area, haemoglobin, albumin, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI) and chemotherapy drugs. Parameter estimates were scaled and summed to create the risk score. The scores were grouped into four: low, intermediate, high and very high risk.
Results
Among 8,585 patients, 467 experienced FN, incidence rate/30 person-days 0.05 (95% CI, 0.05–0.06). Age (1 point if > 65 years), albumin (1 point if < 39 g/L), CCI (1 point if > 2) and chemotherapy (range -5 to 6 points/drug) predicted FN. Median score at inclusion was 2 points (range –5 to 9). The cumulative incidence and the incidence rates and hazard ratios of FN are shown in Figure 1 and Table 1, respectively.
Conclusion
We developed a risk score to predict FN the first month after initiation of chemotherapy. The score is easy to use and provides good differentiation of risk groups; the score needs independent validation before routine use.
Disclosures
All authors: No reported disclosures.
Abstract
Background
Transplant recipients are an immunologically vulnerable patient group and are at elevated risk of Clostridioides difficile infection (CDI) compared with other hospitalized ...populations. However, risk factors for CDI post-transplant are not fully understood.
Methods
Adults undergoing solid organ (SOT) and hematopoietic stem cell transplant (HSCT) from January 2010 to February 2017 at Rigshospitalet, University of Copenhagen, Denmark, were retrospectively included. Using nationwide data capture of all CDI cases, the incidence and risk factors of CDI were assessed.
Results
A total of 1687 patients underwent SOT or HSCT (1114 and 573, respectively), with a median follow-up time (interquartile range) of 1.95 (0.52–4.11) years. CDI was diagnosed in 15% (164) and 20% (114) of the SOT and HSCT recipients, respectively. CDI rates were highest in the 30 days post-transplant for both SOT and HSCT (adjusted incidence rate ratio aIRR, 6.64; 95% confidence interval CI, 4.37–10.10; and aIRR, 2.85; 95% CI, 1.83–4.43, respectively, compared with 31–180 days). For SOT recipients, pretransplant CDI and liver and lung transplant were associated with a higher risk of CDI in the first 30 days post-transplant, whereas age and liver transplant were risk factors in the later period. Among HSCT recipients, myeloablative conditioning and a higher Charlson Comorbidity Index were associated with a higher risk of CDI in the early period but not in the late period.
Conclusions
Using nationwide data, we show a high incidence of CDI among transplant recipients. Importantly, we also find that risk factors can vary relative to time post-transplant.
Impact of CMV Blips in Transplant Recipients Lodding, Isabelle Paula; Mocroft, Amanda; Bang, Caspar Da Cunha ...
Open forum infectious diseases,
10/2017, Volume:
4, Issue:
suppl_1
Journal Article
Peer reviewed
Open access
Abstract
Background
Management of CMV infection in solid organ transplantation (SOT) and haematopoietic stem cell transplantation (HSCT) recipients mainly relies on screening of emerging CMV DNA in ...plasma or whole blood by PCR. However, a first positive CMV PCR may not be reproducible, but constitute a CMV blip (single positive CMV PCR measurements). Such blips are known from monitoring of other viral infections using PCR technology, and may either constitute a false positive read due to assay variability or reflect transient low-level viral replication. We investigated the impact of CMV blips in a cohort of SOT and HSCT recipients.
Methods
SOT and HSCT recipients transplanted between 2010 and 2015, who had a known donor (D)/recipient (R) CMV IgG serostatus (D+/R+, D+/R- or D-/R+), and with ≥3 CMV PCRs fulfilling the CMV PCR triplicate criteria (Figure 1) were included (N = 851). Odds ratio (OR) for factors associated with a triplicate being a blip was estimated by binomial regression adjusted for repeated measurements. Whether blips affected the hazard ratio (HR) for subsequent CMV infection was determined with a Cox model.
Results
851 transplant recipients generated 3883 CMV PCR triplicates (104 blips, 307 infections, 3472 negatives, Figure 1). In the 411 positive triplicates, the OR of a triplicate being a blip decreased with increasing CMV viral load of the second measurement (vs. = 273 IU/mL; >273–910 IU/mL: OR 0.2 95% CI 0.1–0.4, >910 IU/mL: OR 0.07 95% CI 0.03–0.2, P < 0.0001) and was elevated in recipients with intermediary/low-risk CMV IgG serostatus (vs. those with high OR 2.2 95% CI 1.3–3.6 P = 0.003). If the cumulative exposure to viremia in the CMV blips was >910 IU/mL, there was a higher risk of subsequent CMV infection (HR 4.6 95% CI 1.2–17.2 P = 0.02) (Figure 2).
Conclusion
CMV blips are frequent while screening transplant recipients with CMV PCR. CMV blips >910 IU/mL is a risk factor for subsequent infection, indicating that CMV blips at least partly reflect transient low-level CMV infection in transplant recipients. These observations suggest that first positive CMV PCR results should be confirmed before initiation of anti-CMV treatment, especially if the viral load of the first positive PCR is <910 IU/mL, or if the patient has intermediary/low-risk serostatus.
Disclosures
All authors: No reported disclosures.
Abstract
Background
Correct classification of underlying causes of death is an important outcome among transplant recipients. Deaths due to infections and adverse graft function are often ...misclassified. We aimed to develop and validate a system to code causes of death in hematopoietic stem cell (HSCT) and solid organ (SOT) transplant recipients and to identify characteristics that could identify deaths with a clear cause.
Methods
A standardized case record form (CRF) was used to collect clinical information from consecutive recipients transplanted at a transplant hospital between 2004 and 2014, who died 2010–2013 (derivation cohort). Causes of death were determined through a centralized process involving two medical experts who independently assessed each CRF. Factors associated with independent agreement between reviewers were assessed and validated on patients who died 2013–2016 (validation cohort). All cases without agreement were adjudicated. Death causes ascertained by our system were compared with death causes in the Danish National Death Registry.
Results
388 transplant recipients died during 2010 to 2016 (196 (51%) SOT and 192 (49%) HSCT). Using our methodology, leading underlying causes of death among SOT and HSCT were classified as cancer (20%, 48%), graft rejection/failure/graft-vs.-host-disease (35%, 28%) and infections (20%, 11%) (Figure 1). Death causes were in agreement with the Danish registry in only 37% of cases in derivation cohort. In the derivation cohort, kappa was 0.64 (95% CI 0.56–0.69) for independent agreement between the two experts (all remaining classified upon adjudication) (Figure 2). Odds for independent agreement were higher in cases with a history of cancer (aOR 3.20 (1.45–7.06)); among 174 with this characteristics, kappa was 0.71 (0.64–0.79). These findings were reproducible in the validation cohort (kappa for overall independent agreement=0.63 (0.52 – 0.73); among 54 recipients with a history of cancer, kappa = 0.71 (0.57–0.84)).
Conclusion
We developed and validated a method able to systematically and reliably classify underlying cause of death among transplant recipients. There was a high degree of discordance between this classification and that in the National Death Registry. Our method can be applied to any cohort.
Disclosures
All authors: No reported disclosures.
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