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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 To address the hospital bed demand for Delta and Omicron surges in Singapore, the National University Health System (NUHS) developed a COVID Virtual Ward to relieve bed pressures on its three acute hospitals—National University Hospital, Ng Teng Fong General Hospital, Alexandra Hospital. To serve a multilingual population, the COVID Virtual Ward featuring protocolized teleconsultation of high-risk patients, use of a vital signs chatbot, supplemented by home visits where necessary. This study aims to evaluate the safety, outcomes and utilisation of the Virtual Ward as a scalable response to COVID-19 surges. Methods This is a retrospective cohort study of all patients admitted to the COVID Virtual Ward between 23 September to 9 November 2021. Patients were defined as “early discharge” if they were referred from inpatient COVID-19 wards and “admission avoidance” if they were referred directly from primary care or emergency services. Patient demographics, utilisation measures and clinical outcomes were extracted from the electronic health record system. The primary outcomes were escalation to hospital and mortality. Use of the vital signs chatbot was evaluated by examining compliance levels, need for automated reminders and alerts triggered. Patient experience was evaluated using data extracted from a quality improvement feedback form. Results 238 patients were admitted to the COVID Virtual Ward from 23 September to 9 November, of whom 42% were male, 67.6% of Chinese ethnicity. 43.7% were over the age of 70, 20.5% were immunocompromised, and 36.6% were not fully vaccinated. 17.2% of patients were escalated to hospital and 2.1% of patients died. Patients who were escalated to hospital were more likely to be immunocompromised or to have a higher ISARIC 4C-Mortality Score. There were no missed deteriorations. All patients received teleconsults (median of 5 teleconsults per patient, IQR 3–7). 21.4% of patients received home visits. 77.7% of patients engaged with the vital signs chatbot, with a compliance rate of 84%. All patients would recommend the programme to others in their situation. Conclusions Virtual Wards are a scalable, safe and patient-centered strategy to care for high risk COVID-19 patients at home. Trial Registration NA.

Introduction: Hospital-at-home programmes are well described in the literature but not in Asia. We describe a home-based inpatient substitutive care programme in Singapore, with clinical and patient-reported outcomes. Methods: We conducted a retrospective cohort study of patients admitted to a hospital-at-home programme from September 2020 to September 2021. Suitable patients, who otherwise required hospitalisation, were admitted to the programme. They were from inpatient wards, emergency department and community nursing teams in the western part of Singapore, where a multidisciplinary team provided hospital-level care at home. Electronic health record data were extracted from all patients admitted to the programme. Patient satisfaction surveys were conducted post-discharge. Results: A total of 108 patients enrolled. Mean age was 67.9 (standard deviation 16.7) years, and 46% were male. The main diagnoses were skin and soft tissue infections (35%), urinary tract infections (29%) and fluid overload (18%). Median length of stay was 4 (interquartile range 3–7) days. Seven patients were escalated back to the hospital, of whom 2 died after escalation. One patient died at home. There was 1 case of adverse drug reaction and 1 fall at home, and no cases of hospital-acquired infections. Patient satisfaction rates were high and 94% of contactable patients would choose to participate again. Conclusion: Hospital-at-home programmes appear to be safe and feasible alternatives to inpatient care in Singapore. Further studies are warranted to compare clinical outcomes and cost to conventional inpatient care. Keywords: Home-based, hospital-at-home, hospital care, internal medicine, public health

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.

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models.

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.

Introduction: Hospital-at-Home (HaH) programmes are well-established in Australia, Europe, and the United States. However, there is limited experience in Asia, where the hospital is traditionally seen as a safe and trusted space for healing. This cross-sectional study aimed to explore attitudes and perceptions among patients and caregivers in Singapore toward this care model.Methods: A quantitative study design was adopted to collect data among patients and their caregivers from medical wards within two acute hospitals in Singapore. Using a series of closed-ended and open-ended questions, the investigator-administered survey aimed to explore barriers and facilitators determining patients' and caregivers' responses. The study questionnaire was pretested and validated. Data were summarised using descriptive statistics, and logistic regression was performed to determine key factors influencing patients' decisions to enrol in such programmes.Results: Survey responses were collected from 120 participants (101 patients, 19 caregivers; response rate: 76%), of which 87 respondents (72.5%) expressed willingness to try HaH if offered. Many respondents valued non-quantifiable programme benefits, including perceived gains in quality of life. Among them, reasons cited for acceptance included preference for the comfort of their home environment, presence of family members, and confidence toward remote monitoring modalities. Among respondents who were unwilling to accept HaH, a common reason indicated was stronger confidence toward hospital care.Discussion: Most patients surveyed were open to having acute care delivered in their home environment, and concerns expressed may largely be addressed by operational considerations. The findings provide useful insights toward the planning of HaH programmes in Singapore.

ObjectivesThe COVID-19 outbreak in Singapore has largely centred around migrant worker dormitories, comprising over 90% of all cases in the country. Dormitories are home to a culturally and linguistically distinct, low-income population, without on-site healthcare after-hours. The primary objective of this study was to assess the engagement and utilisation of a simple, low-cost, accessible, mobile health solution for remote self-reporting of vital parameters in dormitory residents with COVID-19.DesignRetrospective review of medical care.SettingTwo large migrant worker dormitories with a combined population of 31 546.ParticipantsAll COVID-19-affected residents housed in dormitories during the study period.InterventionAll residents were taught to use a chat assistant to self-report their temperature, heart rate and oxygen saturations. Results flowed into a dashboard, which alerted clinicians of abnormal results.OutcomesThe primary outcome measure was engagement rate. This was derived from the total number of residents who registered on the platform over the total number of COVID-19-affected residents in the dormitories during the study period. Secondary outcome measures included outcomes of the alerts and subsequent escalations of care.Results800 of the 931 COVID-19-affected residents (85.9%) engaged with the platform to log a total of 12 511 discrete episodes of vital signs. Among 372 abnormal readings, 96 teleconsultations were initiated, of which 7 (1.8%) were escalated to emergency services and 18 (4.9%) were triaged to earlier physical medical review on-site.ConclusionsA chat-assistant-based self-reporting platform is an effective and safe community-based intervention to monitor marginalised populations with distinct cultural and linguistic backgrounds, living communally and affected by COVID-19. Lessons learnt from this approach may be applied to develop safe and cost-effective telemedicine solutions across similar settings.

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.

Objective Severity of illness scores used in critical care for benchmarking, quality assurance and risk stratification have been mainly created in high-income countries. In low and middle-income countries (LMICs), they cannot be widely utilized due to the demand for large amounts of data that may not be available (e.g. laboratory results). We attempt to create a new severity prognostication model using fewer variables that are easier to collect in an LMIC. Setting Two intensive care units, one private and one public, from Sao Paulo, Brazil Patients An ICU for the first time. Interventions None. Measurements and Mains results The dataset from the private ICU was used as a training set for model development to predict in-hospital mortality. Three different machine learning models were applied to five different blocks of candidate variables. The resulting 15 models were then validated on a separate dataset from the public ICU, and discrimination and calibration compared to identify the best model. The best performing model used logistic regression on a small set of 10 variables: highest respiratory rate, lowest systolic blood pressure, highest body temperature and Glasgow Coma Scale during the first hour of ICU admission; age; prior functional capacity; type of ICU admission; source of ICU admission; and length of hospital stay prior to ICU admission. On the validation dataset, our new score, named SEVERITAS, had an area under the receiver operating curve of 0.84 (0.82 – 0.86) and standardized mortality ratio of 1.00 (0.91–1.08). Moreover, SEVERITAS had similar discrimination compared to SAPS-3 and better discrimination than the simplified TropICS and R-MPM. Conclusions Our study proposes a new ICU mortality prediction model using simple logistic regression on a small set of easily collected variables may be better suited than currently available models for use in low and middle-income countries.

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.

BACKGROUND Apparent increase in mortality associated with being admitted to hospital on a weekend compared to weekdays has led to controversial policy changes to weekend staffing in the United Kingdom. Studies in the United States have been inconclusive and diagnosis specific, and whether to implement such changes is subject to ongoing debate. OBJECTIVE To compare mortality, length of stay, and cost between patients admitted on weekdays and weekends. DESIGN Retrospective cohort study. SETTING National Inpatient Sample, an administrative claims database of a 20% stratified sample of discharges from all hospitals participating in the Healthcare Cost and Utilization Project. PATIENTS Adult patients who were emergently admitted from 2012 to 2014. INTERVENTION The primary predictor was whether the admission was on a weekday or weekend. MEASUREMENTS The primary outcome was in-hospital mortality and secondary outcomes were length of stay and cost. RESULTS We included 13,505,396 patients in our study. After adjusting for demographics and disease severity, we found a small difference in inpatient mortality rates on weekends versus weekdays (odds ratio [OR] 1.029; 95% confidence interval [CI], 1.020-1.039; P < .001). There was a statistically significant but clinically small decrease in length of stay (2.24%; 95% CI, 2.16-2.33; P < .001) and cost (1.14%; 95% CI, 1.05-1.24; P < .001) of weekend admissions. A subgroup analysis of the most common weekend diagnoses showed substantial heterogeneity between diagnoses. CONCLUSIONS Differences in mortality of weekend admissions may be attributed to underlying differences in patient characteristics and severity of illness and is subject to large between-diagnoses heterogeneity. Increasing weekend services may not result in desired reduction in inpatient mortality rate.