Combination treatment of persistent COVID-19 in immunocompromised patients with remdesivir, nirmaltrevir/ritonavir and tixegavimab/cilgavimab.

We present a retrospective study on the treatment outcomes of severely immunocompromised patients with persistent COVID-19. The study analyzed data from 14 patients who received a combination of tixegavimab/cilgavimab and antiviral medications. Response was evaluated based on symptom improvement, PCR cycle-threshold values, and C-reactive protein levels. Eleven patients achieved complete clinical and virological resolution, while three showed partial responses. The study suggests a potential association between non-response and tixegavimab/cilgavimab neutralization. The findings underscore the need for tailored treatment approaches and further research on optimal strategies for managing persistent COVID-19, as well as the development of antivirals and variant-specific monoclonal antibodies.

A Nationwide Digital Multidisciplinary Intervention Aimed at Promoting Pneumococcal Vaccination in Immunocompromised Patients.

Immunocompromised patients (IPs) are at high risk for infections, some of which are vaccine-preventable. The Israeli Ministry of Health recommends pneumococcal conjugate vaccine 13 (PCV13) and pneumococcal polysaccharide vaccine 23 (PPSV23) for IP, but vaccine coverage is suboptimal. We assessed the project's effectiveness in improving the pneumococcal vaccination rate among IP. An automated population-based registry of IP was developed and validated at Maccabi Healthcare Services, an Israeli health maintenance organization serving over 2.6 million members. Included were transplant recipients, patients with asplenia, HIV or advanced kidney disease; or those receiving immunosuppressive therapy. A personalized electronic medical record alert was activated reminding clinicians to consider vaccination during IP encounters. Later, IP were invited to get vaccinated via their electronic patient health record. Pre- and post-intervention vaccination rates were compared. Between October 2019 and October 2021, overall PCV13 vaccination rates among 32,637 IP went up from 11.9% (n = 3882) to 52% (n = 16,955) (p < 0.0001). The PPSV23 vaccination rate went up from 39.4% (12,857) to 57.1% (18,652) (p < 0.0001). In conclusion, implementation of targeted automated patient- and clinician-facing alerts, a remarkable increase in pneumococcal vaccine uptake was observed among IP. The outlined approach may be applied to increase vaccination uptake in large health organizations.

Clinical impact of the accelerate PhenoTest® BC system on patients with gram-negative bacteremia and high risk of antimicrobial resistance: a prospective before-after implementation study.

BACKGROUND: The Accelerate PhenoTest® BC system (AXDX) is a novel assay for rapid bacterial identification and antimicrobial susceptibility (AST). We report an evaluation of its impact on treatment of patients with Gram-negative bacteremia (GNB) with a high risk of antimicrobial resistance (AMR). METHODS: A prospective single-center evaluation before and after implementation of AXDX in addition to standard-of-care (SOC) microbiology and antimicrobial stewardship program (ASP). Patients with GNB reported during laboratory working hours and prespecified risk factors for AMR were included. The primary outcome was an ASP-oriented beneficial antimicrobial change, defined as either an escalation of an inappropriate empiric treatment or de-escalation of a broad-spectrum treatment of a susceptible organism. Main secondary outcomes were time to an appropriate treatment, antimicrobial treatment duration, length of stay (LOS) and mortality. RESULTS: Included were 46 and 57 patients in the pre- and post-intervention periods, respectively. The median time to an AST-oriented beneficial change was 29.2 h vs. 49.6 h, respectively (p < 0.0001). There were no significant differences in the time to appropriate treatment, LOS or mortality. Antimicrobial treatment duration was longer during the intervention period (10 vs. 8 days, p = 0.007). AXDX failed to correctly identify pathogens in all 6 cases of polymicrobial bacteremia. In two cases patient care was potentially compromised due to inappropriate de-escalation. CONCLUSIONS: AXDX implementation resulted in a 20.4-hour shorter time to an ASP-oriented beneficial antimicrobial change. This should be weighed against the higher costs, the lack of other proven clinical benefits and the potential harm from mis-identification of polymicrobial bacteremias.

Vaccination Coverage among Immunocompromised Patients in a Large Health Maintenance Organization: Findings from a Novel Computerized Registry.

Immune-compromised patients (IPs) are at high risk for infections, some of which are preventable by vaccines. Specific vaccines are recommended for IP; however, the vaccination rate is suboptimal. The aim of this study is to describe the development of an IP registry and to assess vaccination rates in this population. A population-based registry of IPs was developed using an automated extraction of patient electronic health-record data in Maccabi Healthcare Services (MHS), an Israeli health maintenance organization serving over 2.4 million members. Included in the registry were patients receiving immunosuppressive therapy (IT); patients living with HIV (PLWH); solid organ and bone marrow transplant recipients (TR); patients with advanced kidney disease (AKD), and asplenic patients. We evaluated the full schedule for each vaccine's uptake rates for influenza, pneumococcal, meningococcal, and hepatitis B. On 1 October 2019, 32,637 adult immune-compromised patients were identified by the registry. Of them, 1647 were PLWH; 2354 were asplenic; 5317 had AKD; 23,216 were on IT; and 1824 were TR. Their mean age was 57 and 52.4% were females. The crude rate of immune compromise among adult MHS members was 2%. Vaccine coverage rate was overall low for PCV13, with only 11.9% of all IPs in the registry having received one dose. Influenza and PPV23 vaccination rates were higher (45% and 39.4%, respectively). Only 5.3% of all IPs received all three vaccines. Overall, low vaccination coverage was found among IPs. Our registry can serve to identify target-patient populations for interventions and monitor their effectiveness.

Oropharyngeal shedding of herpesviruses before and after BNT162b2 mRNA vaccination against COVID-19.

INTRODUCTION: Concerns were raised over an increase in Bell's palsy, herpes simplex and herpes zoster after BNT162b2 vaccination, all are manifestations of herpesviruses reactivation. As herpesviruses commonly reactivate in the oropharynx, we have hypothesized that oropharyngeal shedding of herpesviruses will increase after vaccination. METHODS: Immune-competent Adults, excluding those using topical steroids or manifesting symptomatic herpesvirus infection, were sampled before BNT162b2 vaccination and one week after. Herpesviruses 1-7 shedding was tested with a multiplexed PCR. RESULTS: In 103 paired samples the prevalence of herpesviruses was similar before and after vaccination: HSV1, 3.9% vs. 5.8% (p = 0.75); HSV2, 0% vs. 1% (p = not applicable, NA); VZV, 0% vs. 0% (p = NA); EBV, 14.6% vs. 17.5% (p = 0.63); CMV, 0% vs. 0% (p = NA); HHV6, 4.9% vs. 7.8% (p = 0.55); HHV7, 71.8% vs. 72.8% (p = 1); any herpesvirus, 73.8% vs. 74.8% (p = 1). DISCUSSION: We did not find evidence for increased oropharyngeal reactivation of herpesviruses one week after BNT162b2.

Epidemiology of Inflammatory Bowel Diseases in Israel: A Nationwide Epi-Israeli IBD Research Nucleus Study.

BACKGROUND: There are currently no nationwide data on the epidemiology of inflammatory bowel diseases (IBD) in Israel. We aimed to determine the population-based epidemiological trends of IBD in the diverse Israeli population. METHODS: Health-administrative data were retrieved from all 4 Israeli health maintenance organizations, insuring 98% of the population, using validated identification algorithms. National trends were determined using Joinpoint regression analysis calculating annual percent change and average annual percent change (AAPC). RESULTS: By 2019, there were 46,074 patients with IBD in Israel, corresponding to a national prevalence of 519/100,000 (0.52%), of whom 54.1% had Crohn disease (CD) and 45.9% had ulcerative colitis (UC). The number of Jewish patients doubled from 18,701 in 2005 (354/100,000) to 38,950 (589/100,000) in 2018 (AAPC, +4.0%; P < 0.05), and the number of Arab patients increased 3-fold from 1096 (102.1/100,000) to 3534 (240.7/100,000; AAPC, +6.8%; P < 0.05) during the same years. However, the increase rate has gradually decelerated over time (annual percent change during 2005-2008, 2009-2014, and 2005-2018 was +6.7%, +4.2%, and +2.3%, respectively; P < 0.05). Pediatric prevalence increased from 37.4 to 52.2/100,000, with CD predominating in both Jews and Arabs. The incidence of CD remained stable (from 15.9/100,000 to 14.9/100,000) and the incidence of UC decreased (15.4/100,000 to 10.5/100,000 (AAPC, -3.2%; P < 0.001)). In contrast, pediatric incidence of CD increased from 7.3/100,000 to 8.3/100,000 (AAPC, +1.9%; P < 0.05) and that of UC increased from 2.6 to 4.4/100,000 (AAPC, +5.8%; P < 0.05). CONCLUSIONS: The IBD prevalence rate in Israel is still increasing but gradually decelerating, probably due to the decreasing overall IBD incidence. Nonetheless, incidence rate in children is still increasing. Ongoing narrowing in the rates between Jews and Arabs over time may indicate shared environmental factors.

Development and validation of a knowledge-driven risk calculator for critical illness in COVID-19 patients.

Facing the novel coronavirus disease (COVID-19) pandemic, evidence to inform decision-making at all care levels is essential. Based on the results of a study by Petrilli et al., we have developed a calculator using patient data at admission to predict critical illness (intensive care, mechanical ventilation, hospice care, or death). We report a retrospective validation of the calculator on 145 consecutive patients admitted with COVID-19 to a single hospital in Israel. Despite considerable differences between the original and validation study populations, of 18 patients with critical illness, 17 were correctly identified (sensitivity: 94.4%, 95% CI, 72.7%-99.9%; specificity: 81.9%, 95% CI, 74.1%-88.2%). Of 127 patients with non-critical illness, 104 were correctly identified. Our results indicate that published knowledge can be reliably applied to assess patient risk, potentially reducing the cognitive burden on physicians, and helping policymakers better prepare for future needs.

Empiric antibiotic protocols for cancer patients with neutropenia: a single-center study of treatment efficacy and mortality in patients with bacteremia.

BACKGROUND: There are several empiric antibiotic treatment options for febrile neutropenia, yet there is no universally-accepted initial protocol. We aimed to assess the performance of a protocol (piperacillin, gentamicin and cefazolin) introduced over 40 years ago and compare its coverage against bacteria isolated from blood of neutropenic patients with that of various commonly used antibiotic treatment protocols. METHODS: Adults with neutropenia admitted between 2003 and 2012 to the hemato-oncologic departments and in whom blood cultures were taken on admission were included. Appropriateness of several common antibiotic protocols was assessed based on the susceptibility of the blood isolates. Crude mortality rates were computed by the susceptibility of bacteria isolated from patients' blood to the actual treatment given. RESULTS: In total, 180 admissions of neutropenic patients (95 in patients who had fever above 38 °C) with positive blood cultures were analyzed. The actual antibiotic regimen prescribed was deemed appropriate in 82% of bacteremia episodes. The recommended institutional protocol was used in 62% of bacteremia episodes in neutropenic patients. This protocol would have been appropriate in 85% of all neutropenic bacteremia episodes and 89% of episodes in febrile neutropenia patients compared with piperacillin/tazobactam (79%, P = 0.13 and 76%, P = 0.002, respectively) and imipenem (93%, P = 0.004 and 92%, P = 0.74, respectively). Isolation of bacteria resistant to the actual antibiotic treatment given was associated with higher mortality at one week and at 30 days. CONCLUSION: Common current antibiotic regimens provide similar coverage among febrile neutropenic patients, whereas broad spectrum antibiotic combinations maximize coverage among neutropenic patients.

Second thoughts on the final rule: An analysis of baseline participant characteristics reports on ClinicalTrials.gov.

BACKGROUND: ClinicalTrials.gov is valuable for aggregate-level analysis of trials. The recently published final rule aims to improve reporting of trial results. We aimed to assess variability in ClinicalTirals.gov records reporting participants' baseline measures. METHODS AND FINDINGS: The September 2015 edition of the database for Aggregate Analysis of ClinicalTrials.gov (AACT), was used in this study. To date, AACT contains 186,941 trials of which 16,660 trials reporting baseline (participant) measures were analyzed. We also analyzed a subset of 13,818 Highly Likely Applicable Clinical Trials (HLACT), for which reporting of results is likely mandatory and compared a random sample of 30 trial records to their journal articles. We report counts for each mandatory baseline measure and variability reporting in their formats. The AACT dataset contains 8,161 baseline measures with 1206 unique measurement units. However, of these 6,940 (85%) variables appear only once in the dataset. Age and Gender are reported using many different formats (178 and 49 respectively). "Age" as the variable name is reported in 60 different formats. HLACT subset reports measures using 3,931 variables. The most frequent Age format (i.e. mean (years) ± sd) is found in only 45% of trials. Overall only 4 baseline measures (Region of Enrollment, Age, Number of Participants, and Gender) are reported by > 10% of trials. Discrepancies are found in both the types and formats of ClinicalTrials.gov records and their corresponding journal articles. On average, journal articles include twice the number of baseline measures (13.6±7.1 (sd) vs. 6.6±7.6) when compared to the ClinicalTrials.gov records that report any results. CONCLUSIONS: We found marked variability in baseline measures reporting. This is not addressed by the final rule. To support secondary use of ClinicalTrials.gov, a uniform format for baseline measures reporting is warranted.

Clinical Trials.Gov: A Topical Analyses.

ClinicalTrials.gov was established as a web-based registry for clinical trials of human participants in 2000. Mandatory registration started in 2008. Given more than a decade of registered trials, it's important to understand the "topic" areas and their evolution over time from this resource. This information may help in identifying current knowledge gaps. We use dynamic topic model (DTM) methods to discover topics and their evolution over last 17 years. Our model suggests that there are disease or organ specific trials such as 'Cardiovascular disorders', Heart & Brain conditions', or 'Breast & Prostate cancer' as well as trials registered for general health. General health trials are less likely to be FDA regulated, but both health and pain management, as well as surgical, heart, and brain trials have upward trend in recent years while advanced cancer trials have downward trended. Our model derives unique insights from metadata associated with each topic area.

A Learning Health Care System Using Computer-Aided Diagnosis.

Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.

Computer-aided assessment of the generalizability of clinical trial results.

BACKGROUND: The effects of an intervention on patients from populations other than that included in a trial may vary as a result of differences in population features, treatment administration, or general setting. Determining the generalizability of a trial to a target population is important in clinical decision making at both the individual practitioner and policy-making levels. However, awareness to the challenges associated with the assessment of generalizability of trials is low and tools to facilitate such assessment are lacking. METHODS: We review the main factors affecting the generalizability of a clinical trial results beyond the trial population. We then propose a framework for a standardized evaluation of parameters relevant to determining the external validity of clinical trials to produce a "generalizability score". We then apply this framework to populations of patients with heart failure included in trials, cohorts and registries to demonstrate the use of the generalizability score and its graphic representation along three dimensions: participants' demographics, their clinical profile and intervention setting. We use the generalizability score to compare a single trial to multiple "target" clinical scenarios. Additionally, we present the generalizability score of several studies with regard to a single "target" population. RESULTS: Similarity indices vary considerably between trials and target population, but inconsistent reporting of participant characteristics limit head-to-head comparisons. CONCLUSION: We discuss the challenges involved in performing automatic assessment of trial generalizability at scale and propose the adoption of a standard format for reporting the characteristics of trial participants to enable better interpretation of their results.

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study.

OBJECTIVE: Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records. STUDY DESIGN AND SETTING: Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders. PARTICIPANTS AND EXPOSURES: Our cohort consisted of more than 40 000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second-line drug classes-sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide-1 agonists-during the years 2000-2015. Roughly, 17 000 of these patients were followed for 12 months after being prescribed a second-line drug. MAIN OUTCOME MEASURES: HbA1c and BMI of these patients after 6 and 12 months following treatment. RESULTS: We demonstrate that all four drug classes reduce HbA1c levels, but the effect of sulfonylureas after 6 and 12 months of treatment is less pronounced compared with other classes. We also estimate that DPP-4 inhibitors decrease body weight significantly more than sulfonylureas and thiazolidinediones. CONCLUSION: Our results are in line with current knowledge on second-line drug effectiveness and effect on BMI. They demonstrate that causal inference from electronic health records is an effective way for conducting multitreatment causal inference studies.

Visual assessment of the similarity between a patient and trial population: Is This Clinical Trial Applicable to My Patient?

BACKGROUND: A critical consideration when applying the results of a clinical trial to a particular patient is the degree of similarity of the patient to the trial population. However, similarity assessment rarely is practical in the clinical setting. Here, we explore means to support similarity assessment by clinicians. METHODS: A scale chart was developed to represent the distribution of reported clinical and demographic characteristics of clinical trial participant populations. Constructed for an individual patient, the scale chart shows the patient's similarity to the study populations in a graphical manner. A pilot test case was conducted using case vignettes assessed by clinicians. Two pairs of clinical trials were used, each addressing a similar clinical question. Scale charts were manually constructed for each simulated patient. Clinicians were asked to estimate the degree of similarity of each patient to the populations of a pair of trials. Assessors relied on either the scale chart, a summary table (aligning characteristics of 2 trial populations), or original trial reports. Assessment time and between-assessor agreement were compared. Population characteristics considered important by assessors were recorded. RESULTS: Six assessors evaluated 6 cases each. Using a visual scale chart, agreement between physicians was higher and the time required for similarity assessment was comparable. CONCLUSION: We suggest that further research is warranted to explore visual tools facilitating the choice of the most applicable clinical trial to a specific patient. Automating patient and trial population characteristics extraction is key to support this effort.

Diagnosis is driven by probabilistic reasoning: counter-point.

Uncertainty is involved in each and every step of the diagnostic process. Trying to eliminate doubt altogether is too costly, is likely to fail, and may lead to patient harm. Acknowledging this, the threshold approach aims to optimize diagnosis-making by adopting the explicit use of probability estimates and by discouraging the pursuit of 100% certainty. Yet physicians are affected by cognitive biases which compromise their probabilistic reasoning and may lead to unreliable estimates. Health informatics tools helping to overcome human limitations by empowering physicians to handle probabilities are needed to increase the efficiency of diagnostic process.

Curating and Integrating Data from Multiple Sources to Support Healthcare Analytics.

As the volume and variety of healthcare related data continues to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate and integrate disparate data from many different sources. We describe our approach to and highlight our experiences with the development of a robust data collection, curation and integration infrastructure that supports healthcare analytics. This system has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiomic data, self-reported data from surveys and self-tracked data from wearable devices from over 600 subjects. The curated data is currently being used to support healthcare analytic applications such as data visualization, patient stratification and predictive modeling.