Spiritual Care of Inpatients Focusing on Outcomes and the Role of Chaplaincy Services: A Systematic Review.

OBJECTIVE: To identify demographic trends associated with patient utilization and healthcare provider request for spiritual care services and to describe the impact of spiritual care on the quality of life (QoL), spiritual well-being (SWB) and level of satisfaction (SAT) of hospitalized patients. PATIENTS AND METHODS: A systematic search of Ovid MEDLINE, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Scopus, was combined with review of relevant bibliographies. A total of 464 titles and abstracts were reviewed. Data were independently extracted by reviewers according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data on the effects of spiritual interventions on QoL, SWB and SAT were extracted, along with demographic data reflecting chaplain services. The results of the studies are presented narratively and in a qualitative manner. RESULTS: Observational or experimental studies investigating chaplain utilization demographics (n = 12), patient satisfaction (n = 9) and QoL/SWB (n = 3) were included. Perceived severity of illness, average length of stay and older age were consistently found to be predictors of higher need for spiritual care. Receipt of spiritual care was correlated with increased patient and family satisfaction, independent of clinical outcome. Chaplain interventions were associated with improvement in perceived QoL and SWB. In spite of this, healthcare workers rarely attempt to explore the patient's or family's need for spiritual care, with the majority of chaplaincy consults occurring in the final day of the patient's life, potentially leading to a failure to meet the spiritual needs of non-terminal patients who have spiritual trauma related to their resolving illnesses. CONCLUSION: Attention to the spiritual needs of hospitalized patients is an essential yet often overlooked aspect of patient care. Chaplains serve as spiritual care specialists whose services can enhance the hospital experience, improve patient satisfaction and help to bridge potential gaps between the patient and medical providers.

Using neural networks to calibrate agent based models enables improved regional evidence for vaccine strategy and policy.

Distribution and administration strategy are critical to successful population immunization efforts. Agent-based modeling (ABM) can reflect the complexity of real-world populations and can experimentally evaluate vaccine strategy and policy. However, ABMs historically have been limited in their time-to-development, long runtime, and difficulty calibrating. Our team had several technical advances in the development of our GradABMs: a novel class of scalable, fast and differentiable simulations. GradABMs can simulate million-size populations in a few seconds on commodity hardware, integrate with deep neural networks and ingest heterogeneous sources. This allows for rapid and real-world sensitivity analyses. Our first epidemiological GradABM (EpiABMv1) enabled simulation interventions over real million-scale populations and was used in vaccine strategy and policy during the COVID-19 pandemic. Literature suggests decisions aided by evidence from these models saved thousands of lives. Our most recent model (EpiABMv2) extends EpiABMv1 to allow improved regional calibration using deep neural networks to incorporate local population data, and in some cases different policy recommendations versus our prior models. This is an important advance for our model to be more effective at vaccine strategy and policy decisions at the local public health level.

Artificial intelligence-enabled screening strategy for drug repurposing in monoclonal gammopathy of undetermined significance.

Monoclonal gammopathy of undetermined significance (MGUS) is a benign hematological condition with the potential to progress to malignant conditions including multiple myeloma and Waldenstrom macroglobulinemia. Medications that modify progression risk have yet to be identified. To investigate, we leveraged machine-learning and electronic health record (EHR) data to screen for drug repurposing candidates. We extracted clinical and laboratory data from a manually curated MGUS database, containing 16,752 MGUS patients diagnosed from January 1, 2000 through December 31, 2021, prospectively maintained at Mayo Clinic. We merged this with comorbidity and medication data from the EHR. Medications were mapped to 21 drug classes of interest. The XGBoost module was then used to train a primary Cox survival model; sensitivity analyses were also performed limiting the study group to those with non-IgM MGUS and those with M-spikes >0.3 g/dl. The impact of explanatory features was quantified as hazard ratios after generating distributions using bootstrapping. Medication data were available for 12,253 patients; those without medications data were excluded. Our model achieved a good fit of the data with inverse probability of censoring weights concordance index of 0.883. The presence of multivitamins, immunosuppression, non-coronary NSAIDS, proton pump inhibitors, vitamin D supplementation, opioids, statins and beta-blockers were associated with significantly lower hazard ratio for MGUS progression in our primary model; multivitamins and non-coronary NSAIDs remained significant across both sensitivity analyses. This work could inform subsequent prospective studies, or similar studies in other disease states.

Blockchain for Electronic Vaccine Certificates: More Cons Than Pros?

Electronic vaccine certificates (EVC) for COVID-19 vaccination are likely to become widespread. Blockchain (BC) is an electronic immutable distributed ledger and is one of the more common proposed EVC platform options. However, the principles of blockchain are not widely understood by public health and medical professionals. We attempt to describe, in an accessible style, how BC works and the potential benefits and drawbacks in its use for EVCs. Our assessment is BC technology is not well suited to be used for EVCs. Overall, blockchain technology is based on two key principles: the use of cryptography, and a distributed immutable ledger in the format of blockchains. While the use of cryptography can provide ease of sharing vaccination records while maintaining privacy, EVCs require some amount of contribution from a centralized authority to confirm vaccine status; this is partly because these authorities are responsible for the distribution and often the administration of the vaccine. Having the data distributed makes the role of a centralized authority less effective. We concluded there are alternative ways to use cryptography outside of a BC that allow a centralized authority to better participate, which seems necessary for an EVC platform to be of practical use.

Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

Objective: To assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs). Patients and Methods: We obtained data on all ED visits at our health care system's largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model's performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and tested across all the sites, and the importance of features was examined to understand the reasons for differing generalizability. Results: The training data sets contained 1918-64,161 ED visits. The AUC for the pooled model ranged from 0.84 to 0.94 across the sites; the performance decreased slightly when Ns were downsampled to match those of our smallest ED site. When site-specific models were trained and tested across all the sites, the AUCs ranged more widely from 0.71 to 0.93. Within a single ED site, the performance of the 5 site-specific models was most variable for our largest and smallest EDs. Finally, when the importance of features was examined, several features were common to all site-specific models; however, the weight of these features differed. Conclusion: A machine learning model for predicting hospital admission from the ED will generalize fairly well within the health care system but will still have significant differences in AUC performance across sites because of site-specific factors.

Medications and Patient Factors Associated With Increased Readmission for Alcohol-Related Diagnoses.

OBJECTIVE: To investigate medication factors and patient characteristics associated with readmissions following alcohol-related hospitalizations. PATIENTS AND METHODS: Adult patients admitted from September 1, 2016, through August 31, 2019, who had an alcohol-related hospitalization were identified through electronic health records. Patient characteristics and medications of interest administered during hospitalization or prescribed at discharge were identified. Medications of interest included US Food and Drug Administration-approved medications for alcohol use disorder, benzodiazepines, barbiturates, gabapentin, opioids, and muscle relaxants. The primary outcome was to identify medications and patient factors associated with 30-day alcohol-related readmission. Secondary outcomes included medications and patient characteristics associated with multiple alcohol-related readmissions within a year from the index admission (ie, two or more readmissions) and factors associated with 30-day all-cause readmission. RESULTS: Characteristics of the 932 patients included in this study associated with a 30-day alcohol-related readmission included younger age, severity of alcohol withdrawal, history of psychiatric disorder, marital status, and the number of prior alcohol-related admission in the previous year. Benzodiazepine or barbiturate use during hospitalization or upon discharge was associated with 30-day alcohol-related readmission (P=.006). Gabapentin administration during hospitalization or upon discharge was not associated with 30-day alcohol-related readmission (P=.079). CONCLUSION: The findings reinforce current literature identifying patient-specific factors associated with 30-day readmissions. Gabapentin use was not associated with readmissions; however, there was an association with benzodiazepine/barbiturate use.

Practical development and operationalization of a 12-hour hospital census prediction algorithm.

Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital's decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/- 3.4% and that this work could be completed in approximately 7 months.

Naltrexone Initiation in the Inpatient Setting for Alcohol Use Disorder: A Systematic Review of Clinical Outcomes.

Alcohol use disorder (AUD) is a highly prevalent health issue in the United States. The number of those receiving medication-assisted treatment (MAT) is limited, despite strong evidence for their effectiveness. The inpatient setting may represent an important opportunity to initiate MAT. The goal of this study was to summarize the data on naltrexone initiation in the emergency department or inpatient setting for the management of AUDs. We searched ClinicalTrials.gov, Ovid EBM Reviews, Ovid Embase, Ovid Medline, Ovid PsycINFO, Scopus, and Web of Science from inception through October 31, 2019. Search strategies were created using a combination of keywords (Supplemental Appendix 1, available online at http://www.mcpiqojournal.org) and standardized index terms related to naltrexone therapy for medically hospitalized patients with AUD. Two uncontrolled pre-post study designs evaluated naltrexone prescription rates, 30-day readmission rates, and rehospitalization rates. Two authors independently abstracted data on study characteristics, results, and study-level risk of bias. The research team collaborated to assess the strength of evidence across studies. Two studies reported that implementing a protocol for naltrexone initiation increased MAT rates, with one study noting a substantial decrease in 30-day hospital readmissions. Overall, we found that there is a paucity of data on naltrexone initiation in the inpatient setting for AUDs. This likely reflects the nature of current clinical practice and prescriber comfortability. There is a need for further studies evaluating MAT initiation in the inpatient setting. Furthermore, efforts to increase provider knowledge of these therapeutic options are in need of further exploration.

Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study.

OBJECTIVE: To estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination. DESIGN: Simulation agent based modeling study. SETTING: Simulated population based on real world US county. PARTICIPANTS: The simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions. INTERVENTIONS: Simulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated. MAIN OUTCOME MEASURES: Cumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days. RESULTS: Over all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested. CONCLUSIONS: A delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

Demystifying airline syncope.

Syncope forms a major part of medical in-flight emergencies contributing one-in-four in-flight medical events accounting to 70% of flight diversions. In such patients, it is important to elucidate the pathophysiology of syncope prior to diversion. Postural hypotension is the most common etiology of in-flight syncopal events. However, individuals without any underlying autonomic dysfunction can still experience syncope from hypoxia also known as airline syncope. Initial steps in managing such patients include positioning followed by the airway, breathing and circulation of resuscitation. These interventions need to be in close coordination with ground control to determine decision for flight diversion. Interventions which have been tried for prevention include mental challenge and increased salt and fluid intake. The current paper enhances the understanding of airline syncope by summarizing the associated pathophysiologic mechanisms and the management medical personnel can initiate with limited resources.

Telemetry: appropriateness of initial assignment and duration in nonintensive setting.

OBJECTIVES: Inappropriate use of telemetry monitoring is associated with alarm fatigue, an increase in health care expenditures, and the potential for patient harm from interventions in clinically inconsequential arrhythmias. We explored adherence to current guidelines for appropriateness of (1) initial telemetry assignment and (2) duration of the assignment. STUDY DESIGN: Retrospective study. METHODS: After institutional review board approval, 695 consecutive adult patients (≥ 18 years) who were admitted with any diagnosis to general medical floors and assigned telemetry at the time of admission over 3 months were enrolled. Patients on surgical service and transferred from critical care were excluded. Data were collected from electronic health records (EHRs). RESULTS: We observed that 155 of 695 (22.3%) patients had been inappropriately assigned telemetry at the time of initial assignment. Of the 540 patients appropriately assigned telemetry, 56.3% of patients had longer than the recommended duration of telemetry monitoring with a median (interquartile range) of 3 (2-4) nonindicated days per patient. The annualized additional cost of telemetry monitoring due to the inefficient utilization was found to be more than $500,000 per year. CONCLUSIONS: Our data further support the need for frequent reassessment of telemetry indication, which can be facilitated by the utilization of EHR-based automated monitoring.

Role of gut microbiota in cardiovascular diseases.

The human gut is colonized by a community of microbiota, primarily bacteria, that exist in a symbiotic relationship with the host. Intestinal microbiota-host interactions play a critical role in the regulation of human physiology. Deleterious changes to the composition of gut microbiota, referred to as gut dysbiosis, has been linked to the development and progression of numerous diseases, including cardiovascular disease (CVD). Imbalances in host-microbial interaction impair homeostatic mechanisms that regulate health and can activate multiple pathways leading to CVD risk factor progression. Most CVD risk factors, including aging, obesity, dietary patterns, and a sedentary lifestyle, have been shown to induce gut dysbiosis. Dysbiosis is associated with intestinal inflammation and reduced integrity of the gut barrier, which in turn increases circulating levels of bacterial structural components and microbial metabolites, including trimethylamine-N-oxide and short-chain fatty acids, that may facilitate the development of CVD. This article reviews the normal function and composition of the gut microbiome, mechanisms leading to the leaky gut syndrome, its mechanistic link to CVD and potential novel therapeutic approaches aimed towards restoring gut microbiome and CVD prevention. As CVD is the leading cause of deaths globally, investigating the gut microbiota as a locus of intervention presents a novel and clinically relevant avenue for future research.

Venous Thromboembolism Prophylaxis: Need for Continuous Assessment Due to Changes in Risk During the Same Hospitalization.

OBJECTIVE: To explore the role of venous thromboembolism (VTE) risk reassessment in hospitalized medically ill patients without a change in level of care. PATIENTS AND METHODS: In this exploratory retrospective study, the medical records of 171 consecutive adult patients (≥18 years) hospitalized under the medicine service for more than 3 days without a change in the level of care from January 1, 2015, to March 1, 2015, were reviewed. The primary outcome was a change in the risk score between day 1 and day 3 of hospital stay (using the Padua Prediction Score). The secondary outcomes were changes in risk stratification class (low vs high) and cost-benefit analysis. RESULTS: The risk score was significantly different between day 1 and day 3 (4.7±1.7 vs 4.2±1.8; P=.008). All the patients with low risk on day 1 remained at low risk on day 3. However, 25 of 136 patients (18.4%) with high risk on day 1 were reclassified as low risk on day 3 (P<.001). No patients changed from low risk to high risk at day 3. The reclassification could have saved $35 per patient-day of inappropriate pharmacological prophylaxis in addition to patient discomfort, bleeding risk, and heparin-induced thrombocytopenia. CONCLUSION: This is the first study to suggest the need for regular assessment for VTE risk on medicine wards because of changing patient risk. Regular reassessment could reduce health care waste and patient discomfort.

Lung injury linked to etanercept therapy.

Etanercept is the first anticytokine drug approved to treat rheumatoid arthritis. Side effects are infrequent, the most common being local skin reactions, headaches, and upper respiratory tract symptoms. We report the first case of lung injury that occurred while receiving this agent. Biopsy specimens of lung and skin lesions demonstrated noncaseating granulomas associated with a microscopic particulate. Withdrawal of etanercept achieved clinical stabilization, and the addition of prednisone resulted in rapid improvement.