Trends in Biochemical Parameters, Healthcare Resource and Medication Use in the 5 Years Preceding IBD Diagnosis: A Health Maintenance Organization Cohort Study.

BACKGROUND: Few data describing pre-diagnosis changes in patients with inflammatory bowel disease (IBD) exist. We aimed to determine if there is a pattern of change in use of health resources, medications and laboratory results in the years preceding diagnosis. METHODS: This retrospective study used electronic medical records of Maccabi Health Services (MHS). Patients with IBD ≥ 16 years of age and minimum of 5-years follow-up were identified by entry into the MHS IBD registry and included in the analysis. Demographic, clinical, medication and laboratory data were collected. Generalized estimating equation model was applied to study trends and compare between years. RESULTS: This study included 5643 patients with IBD. Of these, 3039 (53.8%) had Crohn's disease (CD), 2322 (41.1%) had ulcerative colitis (UC) and 282 (5%) had indeterminate colitis (IC). Laboratory parameters including white blood cells, platelets and C-reactive protein showed significant increases while haemoglobin and mean cell volume showed significant decreases in mean values in the 2 years prior to diagnosis with stable values prior to that (p < 0.0001). Parameters such as creatinine, total protein and albumin showed significant, progressive decreases in mean values starting 5 years prior to diagnosis (p < 0.0001). Patients with CD had distinct laboratory trends when compared with patients with UC. CONCLUSIONS: Changes in laboratory parameters, healthcare service and medication use occur during the 5-year period before IBD diagnosis. These data can have future clinical applicability by developing a composite score and referral algorithm introducing red flags into primary care visits and appropriate referral for specialist care.

Risk Factors for Bacteremia After Endoscopic Procedures in Hospitalized Patients With a Focus on Neutropenia.

BACKGROUND: The risk for bacteremia following endoscopic procedures varies among studies. A low neutrophil count is considered as a risk factor. OBJECTIVE: To assess risk factors for bacteremia following endoscopic procedures, focusing on neutropenia. METHODS: This was a retrospective analysis of all inpatients undergoing endoscopic procedures between 2005 and 2018 with neutrophil count taken within 72 hours before the procedure in a tertiary center in Israel. The primary outcome was positive blood culture within 48 hours following the procedure of bacteria that was not cultured before. Risk factors for bacteremia were assessed and multivariate logistic regression models were built. In neutropenic patients, comparator groups were used to assess the risk related to the procedure and neutropenia. RESULTS: Of 13,168 patients included, postprocedural bacteremia was recorded in 103 (0.8%). Neutropenia, low albumin level, male gender, older age, preprocedure fever, and admitting department were associated with increased risk for bacteremia in both univariate and multivariate analyses. A multivariate model including these factors was found to be predictive of bacteremia (area under the curve 0.82; 95% confidence interval, 0.78-0.88). In neutropenic patients, the risk of postendoscopic bacteremia (4.2%) was not significantly different compared with neutropenic patients undergoing bronchoscopy (1.8%, P=0.14) or from the rate of bacteremia-to-neutropenic episodes ("background risk") in neutropenic patients in general (6.3%, P=0.33). CONCLUSIONS: Postendoscopic bacteremia is a rare event among inpatients. Although neutropenia was found to be a risk factor for bacteremia, it was not higher than the background risk in these patients. Models highly predictive of bacteremia were developed and should be validated.

A Practical Clinical Score Predicting Respiratory Failure in COVID-19 Patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in repeated surges of patients, sometimes challenging triage protocols and appropriate control of patient flow. Available models, such as the National Early Warning Score (NEWS), have shown significant limitations. Still, they are used by some centers to triage COVID-19 patients due to the lack of better tools. OBJECTIVES: To establish a practical and automated triage tool based on readily available clinical data to rapidly determine a distinction between patients who are prone to respiratory failure. METHODS: The electronic medical records of COVID-19 patients admitted to the Sheba Medical Center March-April 2020 were analyzed. Population data extraction and exploration were conducted using a MDClone (Israel) big data platform. Patients were divided into three groups: non-intubated, intubated within 24 hours, and intubated after 24 hours. The NEWS and our model where applied to all three groups and a best fit prediction model for the prediction of respiratory failure was established. RESULTS: The cohort included 385 patients, 42 of whom were eventually intubated, 15 within 24 hours or less. The NEWS score was significantly lower for the non-intubated patients compared to the two other groups. Our improved model, which included NEWS elements combined with other clinical data elements, showed significantly better performance. The model's receiver operating characteristic curve had area under curve (AUC) of 0.92 with of sensitivity 0.81, specificity 0.89, and negative predictive value (NPV) 98.4% compared to AUC of 0.63 with NEWS. As patients deteriorate and require further support with supplemental O2, the need for re-triage emerges. Our model was able to identify those patients on supplementary O2 prone to respiratory failure with an AUC of 0.86 sensitivity 0.95, and specificity 0.7 NPV 98.9%, whereas NEWS had an AUC of 0.76. For both groups positive predictive value was approximately 35. CONCLUSIONS: Our model, based on readily available and simple clinical parameters, showed an excellent ability to predict negative outcome among patients with COVID-19 and therefore might be used as an initial screening tool for patient triage in emergency departments and other COVID-19 specific areas of the hospital.

The National COVID Cohort Collaborative: Analyses of Original and Computationally Derived Electronic Health Record Data.

BACKGROUND: Computationally derived ("synthetic") data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. OBJECTIVE: We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. METHODS: We used the National COVID Cohort Collaborative's instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19-related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. RESULTS: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. CONCLUSIONS: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.

Communication: overcoming the root search problem in complex quantum trajectory calculations.

Three new developments are presented regarding the semiclassical coherent state propagator. First, we present a conceptually different derivation of Huber and Heller's method for identifying complex root trajectories and their equations of motion [D. Huber and E. J. Heller, J. Chem. Phys. 87, 5302 (1987)]. Our method proceeds directly from the time-dependent Schrödinger equation and therefore allows various generalizations of the formalism. Second, we obtain an analytic expression for the semiclassical coherent state propagator. We show that the prefactor can be expressed in a form that requires solving significantly fewer equations of motion than in alternative expressions. Third, the semiclassical coherent state propagator is used to formulate a final value representation of the time-dependent wavefunction that avoids the root search, eliminates problems with caustics and automatically includes interference. We present numerical results for the 1D Morse oscillator showing that the method may become an attractive alternative to existing semiclassical approaches.

Non-adiabatic molecular dynamics with complex quantum trajectories. I. The diabatic representation.

We extend a recently developed quantum trajectory method [Y. Goldfarb, I. Degani, and D. J. Tannor, J. Chem. Phys. 125, 231103 (2006)] to treat non-adiabatic transitions. Each trajectory evolves on a single surface according to Newton's laws with complex positions and momenta. The transfer of amplitude between surfaces stems naturally from the equations of motion, without the need for surface hopping. In this paper we derive the equations of motion and show results in the diabatic representation, which is rarely used in trajectory methods for calculating non-adiabatic dynamics. We apply our method to the first two benchmark models introduced by Tully [J. Chem. Phys. 93, 1061 (1990)]. Besides giving the probability branching ratios between the surfaces, the method also allows the reconstruction of the time-dependent wavepacket. Our results are in quantitative agreement with converged quantum mechanical calculations.

Non-adiabatic molecular dynamics with complex quantum trajectories. II. The adiabatic representation.

We present a complex quantum trajectory method for treating non-adiabatic dynamics. Each trajectory evolves classically on a single electronic surface but with complex position and momentum. The equations of motion are derived directly from the time-dependent Schrödinger equation, and the population exchange arises naturally from amplitude-transfer terms. In this paper the equations of motion are derived in the adiabatic representation to complement our work in the diabatic representation [N. Zamstein and D. J. Tannor, J. Chem. Phys. 137, 22A517 (2012)]. We apply our method to two benchmark models introduced by John Tully [J. Chem. Phys. 93, 1061 (1990)], and get very good agreement with converged quantum-mechanical calculations. Specifically, we show that decoherence (spatial separation of wavepackets on different surfaces) is already contained in the equations of motion and does not require ad hoc augmentation.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: This study sought to evaluate whether synthetic data derived from a national coronavirus disease 2019 (COVID-19) dataset could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original dataset (n = 1 854 968 severe acute respiratory syndrome coronavirus 2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip code-level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely tested zip codes (which contained most of the data) were similar between original and synthetically derived datasets. Analyses of sparsely tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression-an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C).

OBJECTIVE: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. MATERIALS AND METHODS: Using an original data set (n=1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. RESULTS: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean=2.9±2.4; max=16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n=171) and for all unsuppressed zip codes (n=5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. DISCUSSION: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. CONCLUSION: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression -an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Spot the difference: comparing results of analyses from real patient data and synthetic derivatives.

BACKGROUND: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification. OBJECTIVES: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns. METHODS: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3). RESULTS: For each use case, the results of the analyses were sufficiently statistically similar (P > 0.05) between the synthetic derivative and the real data to draw the same conclusions. DISCUSSION AND CONCLUSION: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.

Magnetic excitations in Cu6 and Mn6 hexagons embedded in D3d-symmetric polyoxotungstates.

In this article we reconsider the discussion of the magnetic measurements for the two novel polyoxotungstates, (n-BuNH(3))(12)[(CuCl)(6)(AsW(9)O(33))(2)].6H(2)O and (n-BuNH(3))(12)[(MnCl)(6)(SbW(9)O(33))(2)].6H(2)O, which have been synthesized and characterized by Yamase et al. (Inorg.Chem. 2006, 45, 7698). Analysis of the magnetic susceptibility and magnetization for Cu(6)(12+) and Mn(6)(12+) hexagons based on the exact diagonalization of isotropic exchange Hamiltonian shows that the best-fit first-neighbor coupling parameters are J = 35 and 0.55 cm(-1), respectively, while the second-neighbor interactions are very small. These values exceed considerably those obtained by Yamase et al. (J = 8.82 and 0.14 cm(-1)) on the basis of the Kambe-Van Vleck formula that is inappropriate for six-membered rings. We also got perfect fits to the experimental data for the field dependence of magnetization at 1.8 K. The results imply the importance of axial anisotropy, which is shown to be especially pronounced for the Mn(6)(12+) cluster. We discuss also the symmetry assignments of exchange multiplets to the exact SGamma terms (full spin, S, and irreducible representation, Gamma, of the point group) and correlate the results with the selection rules for the anisotropic magnetic contributions. The antisymmetric exchange is shown to appear in orbitally degenerate multiplets as a first-order perturbation and gives rise to an easy axis of magnetization along the C(6) axis. Evaluation of the Zeeman levels shows that the field applied in the plane of the hexagon fully reduces the effect of the antisymmetric exchange.