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Doctors, authors, funders and hospital managers should take care to distinguish the important differences between hospital in the home (HIH) and outpatient parenteral antimicrobial therapy (OPAT) services. HIH is an inpatient service delivered at home usually by (or on behalf of) hospitals, which aims to substitute for a traditional inpatient stay. It does so by delivering a wide range of hospital treatments to patients at home, or residential aged care, using hospital medical and nursing staff, delivery technologies and venous access, pharmacy, radiology and pathology, and a structured system of on call and governance. OPAT is an outpatient service, usually run through infectious diseases physicians' offices or departments. Most care is delivered in infusion centres and requires patients to travel for their care. Generally, there is no after-hours support. HIH has supplanted the role of OPAT due to improved governance and a wider clinical and severity scope. HIH is accessible from hospital emergency departments or directly from residential aged care facilities. Inpatient capacity has been expanded during the COVID-19 pandemic. There is evidence that both HIH and OPAT can successfully treat their selected patient groups. There are no head-to-head studies, but in observational comparisons there might be more adverse drug events in OPAT. OPAT places a greater onus of care, supervision and travel needs on the patient and family. Where HIH is not available, OPAT may remain an alternative for some patients. However, HIH seeks to redefine the delivery of inpatient care away from the location of care.

Background: Previous studies in Western populations, using immunohistochemistry (IHC) methods to subtype diffuse large B-cell lymphoma (DLBCL), suggest that germinal center B-cell lymphomas (GCBs) have improved outcomes. However, data in Asians have been limited and conflicting. This study aims to evaluate the prognostic impact of cell-of-origin (COO) subtyping by IHC and Lymph2Cx in South-East Asian (SEA) DLBCL patients, and to summarize the existing literature.Methods: A single-center retrospective analysis of 384 DLBCL patients diagnosed 2013-2018 who received Rituximab-based chemotherapy was performed. Hans and Lymph2Cx were used to assign COO and correlated with outcomes.Results: International Prognostic Index (IPI) score was associated with overall survival (OS) and progression-free survival (PFS). The 5-yr-OS for non-GCB versus GCB for COO by Hans was 70% versus 71% p=0.39, while 5-yr-OS for ABC versus GCB for COO by Lymph2Cx was 74% versus 92% p=0.19. The 5-yr-PFS for non-GCB versus GCB for COO by Hans was 65% versus 70% p=0.26, while 5-yr-PFS for ABC versus GCB for COO by Lymph2Cx was 64% versus 86% p=0.07.Conclusions: IPI is reaffirmed to be relevant in the rituximab era. COO by Hans has no prognostic significance, while subtyping by Lymph2Cx trends toward GCBs having better PFS and OS.

OBJECTIVES: Although one third or more of critically ill patients in the United States are obese, obesity is not incorporated as a contributing factor in any of the commonly used severity of illness scores. We hypothesize that selected severity of illness scores would perform differently if body mass index categorization was incorporated and that the performance of these score models would improve after consideration of body mass index as an additional model feature. DESIGN: Retrospective cohort analysis from a multicenter ICU database which contains deidentified data for more than 200,000 ICU admissions from 208 distinct ICUs across the United States between 2014 and 2015. SETTING: First ICU admission of patients with documented height and weight. PATIENTS: One-hundred eight-thousand four-hundred two patients from 189 different ICUs across United States were included in the analyses, of whom 4,661 (4%) were classified as underweight, 32,134 (30%) as normal weight, 32,278 (30%) as overweight, 30,259 (28%) as obese, and 9,070 (8%) as morbidly obese.None. MEASUREMENTS AND MAIN RESULTS: To assess the effect of adding body mass index as a risk adjustment element to the Acute Physiology and Chronic Health Evaluation IV and Oxford Acute Severity of Illness scoring systems, we examined the impact of this addition on both discrimination and calibration. We performed three assessments based upon 1) the original scoring systems, 2) a recalibrated version of the systems, and 3) a recalibrated version incorporating body mass index as a covariate. We also performed a subgroup analysis in groups defined using World Health Organization guidelines for obesity. Incorporating body mass index into the models provided a minor improvement in both discrimination and calibration. In a subgroup analysis, model discrimination was higher in groups with higher body mass index, but calibration worsened. CONCLUSIONS: The performance of ICU prognostic models utilizing body mass index category as a scoring element was inconsistent across body mass index categories. Overall, adding body mass index as a risk adjustment variable led only to a minor improvement in scoring system performance.

The duration and type of dual antiplatelet therapy (DAPT) prescribed to patients after percutaneous coronary intervention (PCI) involves carefully balancing reduced ischemia and increased bleeding risk for individual patients. Whereas multiple bleeding risk scores exist, the performance of these models to predict long-term bleeding in the setting of DAPT across different settings and populations is unclear. Therefore, we performed a systematic review and meta-analysis to compare the performance of current bleeding risk prediction scores for predicting major long-term bleeding events in patients on DAPT post-PCI. Based on a search of MEDLINE (January 1, 1946 to March 3, 2017) and EMBASE (January 1, 1974 to March 3, 2017) for studies published in the English language, we identified 10 published studies of 11 risk unique risk prediction models across a wide variety of settings. Area under the receiver operating characteristic curve (AUC) was used to measure discrimination, when available. Our findings reveal that the prediction models created to date demonstrate only modest accuracy, with the reported AUCs ranging from 0.54 to 0.89; aggregated AUC 0.68 (95% confidence intervals 0.65 to 0.72). Although only 5 studies (50%) reported measures of calibration, the reported models were reasonably well calibrated. Only 3 models (33%) were externally validated. Meta-regression demonstrated lack of influence by age (p = 0.99) or length of follow up (p = 0.42). Sensitivity analysis did not significantly change the results. Novel prediction models are warranted to aid in maximizing the benefit of DAPT after PCI while minimizing harm.

Objective: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness. Design: Retrospective cohort study in a large ICU database. Setting: Tertiary teaching hospital. Patients: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission. Interventions: None. Measurements and Main Results: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27–1.33) × 109/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00–0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02–0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009). Conclusions: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Interval laboratory investigations are necessary for monitoring chronic diseases. However, testing too frequently may not be beneficial clinically and can be considered low-value care. We examined the frequency of glycosylated haemoglobin, lipids, iron panels (serum iron, ferritin, transferrin, iron binding) thyroid function (free T4 and thyroid stimulating hormone) and 25-OH vitamin D tests in a 1290-bed tertiary hospital in Singapore. All tests done over a 20-month period (January 2016 to August 2017) were retrieved from the laboratory database. Of the 275 565 tests done for 115 971 patients, 5.2% were repeat tests done at intervals shorter than the minimum retesting interval, as defined by the Royal College of Pathologist and Irish Guidelines on the Use of the Laboratory. Using the Centers for Medicare and Medicaid Services Clinical Laboratory Fee Schedule, we estimated a cost burden of US$222 096 per year. Strategies to reduce unnecessary repetitive testing can result in significant cost savings.