Citations

Upper and lower limb amputations are frequently associated with phantom limb pain (PLP). Recently, virtual reality (VR) and augmented reality (AR) have been reported as a potential therapy of PLP. We have conducted a systematic review of literature to evaluate the efficacy of VR and AR in managing PLP. Four databases were searched: PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for our organization. The initial search resulted in 164 results. After title, abstract, and full-text screening, 9 studies were included. One study was of good quality and 8 studies were of fair to poor quality. Seven studies utilized VR and 2 studies utilized AR. The number of treatment sessions ranged from 1 to 28 and the duration ranged from 10 minutes to 2 hours. Several pain scales were used to evaluate PLP pre- and postintervention including Numeric Rating Scale, Pain Rating Index, McGill Pain Questionnaire, and Visual Analog Scale. All the studies reported improvement of PLP on one or more of pain scales after one or more sessions of VR or AR. Despite the promising results reported by literature, we cannot recommend using VR or AR for PLP. Most of the studies are of poor design and have limited sample size with high bias levels. Therefore, no substantial evidence can be derived from them. However, we do believe further research with high-quality randomized controlled trials should take place to increase the knowledge of the potential advantages.

AbstractGood physician–patient communication is key for effective hospital care. We describe a patient diagnosed with an acute pulmonary embolism and bacteremia who was treated in a virtual hybrid hospital‐at‐home program. Constant communication with the virtual and in‐home healthcare teams enabled a unified knowledge of the patient's wishes.

Chronically ill patients with superimposed acute illness requiring hospitalization are more likely to develop an extended length of stay, hospital-acquired infections, and adverse events throughout their hospitalization. An excellent alternative to managing this population of patients in the traditional bricks-and-mortal (BAM) hospital is the hospital-at-home (HaH) model. The Advanced Care at Home (ACH) program is Mayo Clinic’s HaH model that provides acute and postacute care to high-acuity patients in their homes rather than in the traditional hospital and skilled nursing facility. We report a case of postoperative care through the ACH program of a patient suffering from short gut syndrome, high-output ileostomy, and severe protein-calorie malnutrition in the setting of previously diagnosed triple-negative invasive ductal carcinoma (IDC) of the right breast complicated by lung and brain metastasis. The patient had multiple complications that required repeated scare escalations directed by a multidisciplinary virtual care. Despite these complications, the ACH model of care was able to keep the patient in the home setting the majority of the time, limiting BAM hospital days, and eliminating the need to use the emergency department for acute escalation for 3 months. The patient was able to recover during this time period and proceed to successful take-down of the ileostomy. This case highlights the benefits of the ACH program by offering high-acuity hospital-level care to severely ill patients in the comfort of their homes. Highly qualified providers paired with curated technology in the home allowed for prompt identification of patient decompensation and timely initiation of treatment while avoiding institutionalization.

Introduction The Care Hotel is a virtual hybrid care model for postoperative patients after low-risk procedures which allow recovery in an outpatient environment. This study aimed to analyze if the American Society of Anesthesiologists Physical Status (ASA PS) Classification System can be used as a predictive factor for staying at Mayo Clinic’s Care Hotel. Methods This retrospective cohort study was conducted between July 23, 2020, and June 4, 2021, at Mayo Clinic in Florida, a 306-bed community academic hospital. ASA PS Class and post-procedure care setting (Care Hotel vs inpatient ward) were collected. Patients were classified into two ASA PS groups (ASA PS Classes 1-2 and 3-4). Pearson’s Chi-square test was used to determine if the ASA PS Class and having stayed or not at the Care Hotel were independent and an Odds Ratio (OR) calculated. Results Out of 392 surgical and procedural patients, 272 (69.39%) chose the Care Hotel and 120 (30.61%) chose the inpatient ward. There was a statistically significant association between ASA PS Class and staying at the Care Hotel, P < .01. The OR of preferring to stay at the Care Hotel in patients with ASA PS Class 1-2 vs ASA PC Class 3-4 was 1.91 ( P = .0041, 95% CI: 1.229-2.982) Conclusion Patients with ASA PS Classes 1-2 are almost twice as likely to elect to stay at the Care Hotel compared to those with ASA PS Classes 3-4. This finding may help care teams focus their Care hotel recruitment efforts.

Background: Breast cancer is the most common malignancy and the second most common cause of death in women worldwide. Axillary lymph node metastasis (ALNM) is the most significant prognostic factor in breast cancer. Under the current guidelines, sentinel lymph node biopsy (SLNB) is the standard of axillary staging in patients with clinically-node negative breast cancer. Despite the minimally invasive nature of SLNB, it can cause short and long-term morbidities including pain, sensory impairment, and upper limb motor dysfunction. However, lymphedema remains the most feared adverse event, and it affects 7% of patients within 36 months of follow-up. Recently, we have witnessed the implication of radiomics and artificial intelligence domains in the diagnosis and follow-up of many malignancies with promising results. Therefore, we have conducted a systematic search to investigate the potentials of radiomics and artificial intelligence in predicting ALNM. Methods: Four electronic databases were searched: PubMed, Scopus, CINAHL, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization. Results: For radiomics, area under the curve (AUC) for the included studies ranged from 0.715 to 0.93. Accuracy ranged from 67.7% to 98%. Sensitivity and specificity ranged from 70.3% to 97.8% and 58.4% to 98.2%, respectively. For other artificial intelligence methods, AUC ranged from 0.68 to 0.98, while accuracy ranged from 55% to 89%. Conclusion: The results of radiomics and artificial intelligence in predicting ALNM are promising. However, validation as a substitute to SLNB requires more substantial evidence from large randomized trials.

Medulloblastoma (MB) is a malignant embryonal tumor of the posterior fossa belonging to the family of primitive neuro-ectodermic tumors (PNET). MB generally occurs in pediatric age, but in 14–30% of cases, it affects the adults, mostly below the age of 40, with an incidence of 0.6 per million per year, representing about 0.4–1% of tumors of the nervous system in adults. Unlike pediatric MB, robust prospective trials are scarce for the post-puberal population, due to the low incidence of MB in adolescent and young adults. Thus, current MB treatments for older patients are largely extrapolated from the pediatric experience, but the transferability and applicability of these paradigms to adults remain an open question. Adult MB is distinct from MB in children from a molecular and clinical perspective. Here, we review the management of adult MB, reporting the recent published literature focusing on the effectiveness of upfront chemotherapy, the development of targeted therapies, and the potential role of a reduced dose of radiotherapy in treating this disease.

AbstractBackgroundMass casualty incidents (MCIs) create an immediate surge in blood product demand. We hypothesize local inventories in major U.S. cities would not meet this demand.Study Design and MethodsA simulated blast in a large crowd estimated casualty numbers. Ideal resuscitation was defined as equal amounts of red blood cells (RBCs), plasma, platelets, and cryoprecipitate. Inventory was prospectively collected from six major U.S. cities at six time points between January and July 2019. City‐wide blood inventories were classified as READY (>1 U/injured survivor), DEFICIENT (<10 U/severely injured survivor), or RISK (between READY and DEFICIENT), before and after resupply from local distribution centers (DC), and features of DEFICIENT cities were identified.ResultsThe simulated blast resulted in 2218 injured survivors including 95 with severe injuries. Balanced resuscitation would require between 950 and 2218 units each RBC, plasma, platelets and cryoprecipitate. Inventories in 88 hospitals/health systems and 10 DCs were assessed. Of 36 city‐wide surveys, RISK inventories included RBCs (n = 16; 44%), plasma (n = 24; 67%), platelets (n = 6; 17%), and cryoprecipitate (n = 22; 61%) while DEFICIENT inventories included platelets (n = 30; 83%) and cryoprecipitate (n = 12; 33%). Resupply shifted most RBC and plasma inventories to READY, but some platelet and cryoprecipitate inventories remained at RISK (n = 24; 67% and n = 12; 33%, respectively) or even DEFICIENT (n = 11; 31% and n = 6; 17%, respectively). Cities with DEFICIENT inventories were smaller (p <.001) with fewer blood products per trauma bed (p <.001).DiscussionIn this simulated blast event, blood product demand exceeded local supply in some major U.S. cities. Options for closing this gap should be explored to optimize resuscitation during MCIs.

Background COVID‐19 is an infectious illness, featured by an increased risk of thromboembolism. However, no standard antithrombotic therapy is currently recommended for patients hospitalized with COVID‐19. The aim of this study was to evaluate safety and efficacy of additional therapy with aspirin over prophylactic anticoagulation (PAC) in patients hospitalized with COVID‐19 and its impact on survival. Methods and Results A total of 8168 patients hospitalized for COVID‐19 were enrolled in a multicenter‐international prospective registry (HOPE COVID‐19). Clinical data and in‐hospital complications, including mortality, were recorded. Study population included patients treated with PAC or with PAC and aspirin. A comparison of clinical outcomes between patients treated with PAC versus PAC and aspirin was performed using an adjusted analysis with propensity score matching. Of 7824 patients with complete data, 360 (4.6%) received PAC and aspirin and 2949 (37.6%) PAC. Propensity‐score matching yielded 298 patients from each group. In the propensity score‐matched population, cumulative incidence of in‐hospital mortality was lower in patients treated with PAC and aspirin versus PAC (15% versus 21%, Log Rank P =0.01). At multivariable analysis in propensity matched population of patients with COVID‐19, including age, sex, hypertension, diabetes, kidney failure, and invasive ventilation, aspirin treatment was associated with lower risk of in‐hospital mortality (hazard ratio [HR], 0.62; [95% CI 0.42–0.92], P =0.018). Conclusions Combination PAC and aspirin was associated with lower mortality risk among patients hospitalized with COVID‐19 in a propensity score matched population compared to PAC alone.