Validation of the Diagnostic Accuracy Levels of International Classification of Diseases, 10th Revision Codes for Diabetic Ketoacidosis: A Multicentre, Cross-sectional Study of Adults

OBJECTIVES

International Classification of Diseases (ICD) codes are commonly used to identify cases of diabetic ketoacidosis (DKA) in health services research, but they have not been validated. Our aim in this study was to assess the accuracy of ICD, 10th revision (ICD-10) diagnosis codes for DKA.

METHODS

We conducted a multicentre, cross-sectional study using data from 5 hospitals in Ontario, Canada. Each hospitalization event has a single most responsible diagnosis code. We identified all hospitalizations assigned diagnosis codes for DKA. A true case of DKA was defined using laboratory values (serum bicarbonate ≤18 mmol/L, arterial pH ≤7.3, anion gap ≥14 mEq/L, and presence of ketones in urine or blood). Chart review was conducted to validate DKA if laboratory values were missing or the diagnosis of DKA was unclear. Outcome measures included positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of ICD-10 codes in patients with laboratory-defined DKA.

RESULTS

We identified 316,517 hospitalizations. Among these, 312,948 did not have an ICD-10 diagnosis code for DKA and 3,569 had an ICD-10 diagnosis code for DKA. Using a combination of laboratory and chart review, we identified that the overall PPV was 67.0%, the NPV was 99.7%, specificity was 99.6%, and sensitivity was 74.9%. When we restricted our analysis to hospitalizations in which DKA was the most responsible discharge diagnosis (n=3,374 [94.5%]), the test characteristics were PPV 69.8%, NPV 99.7%, specificity 99.7%, and sensitivity 71.9%.

CONCLUSION

ICD-10 codes can identify patients with DKA among those admitted to general internal medicine.

Press Releases of Drug-Related Randomized Trial Results Prior to Publication in High-Impact Journals: an Observational Study

IMPORTANCE

Results from high-profile randomized controlled trials (RCTs) are routinely reported through press release months prior to peer-reviewed publication. There are potential benefits to press releases (e.g., knowledge dissemination, ensuring regulatory compliance), but also potential drawbacks (e.g., selective reporting, positive "spin").

OBJECTIVE

To characterize the practice of press release predating the publication of a drug-related RCT in a peer-reviewed journal ("preemptive press release"), including factors associated with this practice.

DESIGN, SETTING, AND PARTICIPANTS

We systematically reviewed all RCTs of medications published between 2015 and 2019 in the New England Journal of Medicine (NEJM), Journal of the American Medical Association (JAMA), and Lancet. Press releases were identified using a systematic search of the grey literature (e.g., press release databases, study sponsor websites). An RCT was considered to have a preemptive press release if the press release was published at least three months (90 days) prior to the date of publication in a peer-reviewed journal.

MAIN OUTCOMES AND MEASURES

Presence of preemptive press release, defined as a press-release at least 90 days prior to the date of publication in a peer-reviewed journal. As secondary measures for dissemination, we also assessed citation count and Altmetric score.

RESULTS

We identified 988 RCTs, of which 172 (17%) had a press release published at least 90 days before the date of peer-reviewed publication. Press releases were published a median of 246 days (interquartile range [IQR] 169-366 days) before publication in a peer-reviewed journal. In the multivariable logistic regression model, the strongest predictor of having a preemptive press release was funding by a pharmaceutical company (odds ratio 13, 95% CI 7, 25). Approximately 85% of RCTs with preemptive press releases had a positive primary outcome and, concordantly, 81% of the corresponding press releases had a positive headline. Multivariable regression models identified studies with a preemptive press release had a similar Altmetric score (median - 15, 95% CI - 33, 12) and higher median citation count (median 22 [95% CI 10 to 33] compared to studies without a preemptive press release.

CONCLUSIONS AND RELEVANCE

Preemptive press releases were common, most often issued for trials funded by a pharmaceutical company, and typically preceded publication in a peer-reviewed journal by approximately eight months.

Comparison of Medical and Mental Health Sequelae Following Hospitalization for COVID-19, Influenza, and Sepsis

Importance

People who survive hospitalization for COVID-19 are at risk for developing new cardiovascular, neurological, mental health, and inflammatory autoimmune conditions. It is unclear how posthospitalization risks for COVID-19 compare with those for other serious infectious illnesses.

Objective

To compare risks of incident cardiovascular, neurological, and mental health conditions and rheumatoid arthritis in 1 year following COVID-19 hospitalization against 3 comparator groups: prepandemic hospitalization for influenza and hospitalization for sepsis before and during the COVID-19 pandemic.

Design, Setting, and Participants

This population-based cohort study included all adults hospitalized for COVID-19 between April 1, 2020, and October 31, 2021, historical comparator groups of people hospitalized for influenza or sepsis, and a contemporary comparator group of people hospitalized for sepsis in Ontario, Canada.

Exposure

Hospitalization for COVID-19, influenza, or sepsis.

Main Outcome and Measures

New occurrence of 13 prespecified conditions, including cardiovascular, neurological, and mental health conditions and rheumatoid arthritis, within 1 year of hospitalization.

Results

Of 379 366 included adults (median [IQR] age, 75 [63-85] years; 54% female), there were 26 499 people who survived hospitalization for COVID-19, 299 989 historical controls (17 516 for influenza and 282 473 for sepsis), and 52 878 contemporary controls hospitalized for sepsis. Hospitalization for COVID-19 was associated with an increased 1-year risk of venous thromboembolic disease compared with influenza (adjusted hazard ratio, 1.77; 95% CI, 1.36-2.31) but with no increased risks of developing selected ischemic and nonischemic cerebrovascular and cardiovascular disorders, neurological disorders, rheumatoid arthritis, or mental health conditions compared with influenza or sepsis cohorts.

Conclusions and Relevance

In this cohort study, apart from an elevated risk of venous thromboembolism within 1 year, the burden of postacute medical and mental health conditions among those who survived hospitalization for COVID-19 was comparable with other acute infectious illnesses. This suggests that many of the postacute consequences of COVID-19 may be related to the severity of infectious illness necessitating hospitalization rather than being direct consequences of infection with SARS-CoV-2.

Observation of high-energy neutrinos from the Galactic plane

The origin of high-energy cosmic rays, atomic nuclei that continuously impact Earth's atmosphere, is unknown. Because of deflection by interstellar magnetic fields, cosmic rays produced within the Milky Way arrive at Earth from random directions. However, cosmic rays interact with matter near their sources and during propagation, which produces high-energy neutrinos. We searched for neutrino emission using machine learning techniques applied to 10 years of data from the IceCube Neutrino Observatory. By comparing diffuse emission models to a background-only hypothesis, we identified neutrino emission from the Galactic plane at the 4.5σ level of significance. The signal is consistent with diffuse emission of neutrinos from the Milky Way but could also arise from a population of unresolved point sources.

Impact of presentation at conference with timed release of academic publication

Fast-tracking publication of original research to coincide with a conference presentation ("coordinated publication") is a mechanism of rapidly disseminating new data. How often this occurs, whether its frequency is changing, and the impact of this approach on information dissemination, is unknown. Our objective was to describe the characteristics of coordinated publications, how the practice has changed over time, and evaluate its potential impact on dissemination of study results. We conducted a cross-sectional study of randomized controlled trials published in NEJM, Lancet, and JAMA between January 1, 2015, and December 31, 2019. Among the 1533 included randomized controlled trials, 502 (33%) had coordinated publications. Coordinated publications increased from 30% [n = 94] in 2015 to 37% [n = 136] in 2019. Coordinated publications were more likely to be unblinded (61% [n = 305] vs. 52% [n = 532]) and more likely to be funded by industry (50% [n = 249] vs. 30% [n = 311]). The strongest predictor of a coordinated publication was cardiovascular disease subspecialty (OR = 3.96, 95% CI [2.95, 5.36]). The median number of citations (188 vs. 98) and the median Altmetric score (318 vs. 182) were higher for coordinated publications than non-coordinated publications. These differences persisted in a multivariable regression model. Coordinated publication is increasingly common. While coordinated publications may generate greater attention, they were observed to be more likely to be unblinded and more likely to be funded by industry, raising questions about the value and intentions of such promotion.

Osmotic Demyelination Syndrome in Patients Hospitalized with Hyponatremia

Osmotic Demyelination Syndrome and HyponatremiaThe authors estimated the frequency of occurrence of osmotic demyelination syndrome (ODS) after correction of hyponatremia (i.e., serum sodium <130 mmol/l). Among 22,858 patients hospitalized for hyponatremia, there were 12 cases of ODS (0.05%). Correction of the serum sodium occurred in 3632 (17.7%) patients, less than one-half of the ODS cases.

The association between diabetes and mortality among adult patients hospitalized with COVID-19: Cohort Study of Hospitalized Adults in Ontario, Canada and Copenhagen, Denmark

Importance

Diabetes has been reported to be associated with an increased risk of death among patients with COVID-19. However, available studies lack detail on COVID illness severity and measurement of relevant comorbidities.

Design, Setting, and Participants

We conducted a multicenter, retrospective cohort study of patients over the age of 18 years who were hospitalized with COVID-19 between January 1, 2020 and November 30, 2020 in Ontario, Canada and Copenhagen, Denmark. Chart abstraction emphasizing co-morbidities and disease severity was performed by trained research personnel. The association between diabetes and death was measured using Poissson regression.

Main Outcomes and Measures

Within hospital 30-day risk of death.

Results

Our study included 1133 hospitalized patients with COVID-19 in Ontario and 305 in Denmark, of whom 405 and 75 patients respectively had pre-existing diabetes. In both Ontario and Denmark, patients with diabetes were more likely to be older, have chronic kidney disease, cardiovascular disease, higher troponin levels, and to receive antibiotics compared with adults who did not have diabetes. In Ontario, 24% (n=96) of adults with diabetes died compared with 15% (n=109) of adults without diabetes. In Denmark, 16% (n=12) of adults with diabetes died in hospital compared with 13% (n=29) among those without diabetes. In Ontario, the crude mortality rate ratio among patients with diabetes was 1.60 [1.24 – 2.07 95% CI] and in the adjusted regression model was 1.19 [0.86 – 1.66 95% CI]. In Denmark, the crude mortality rate ratio among patients with diabetes was 1.27 (0.68 – 2.36 95% CI) and in the adjusted model was 0.87 (0.49 – 1.54 95% CI)]. Meta-analysis of the two rate ratios from each region resulted in a crude mortality rate ratio of 1.55 (95% CI 1.22,1.96) and an adjusted mortality rate ratio of 1.11 (95% CI 0.84, 1.47).

Conclusions

Presence of diabetes was not strongly associated with in-hospital COVID mortality independent of illness severity and other comorbidities.

Evidence for neutrino emission from the nearby active galaxy NGC 1068

A supermassive black hole, obscured by cosmic dust, powers the nearby active galaxy NGC 1068. Neutrinos, which rarely interact with matter, could provide information on the galaxy’s active core. We searched for neutrino emission from astrophysical objects using data recorded with the IceCube neutrino detector between 2011 and 2020. The positions of 110 known gamma-ray sources were individually searched for neutrino detections above atmospheric and cosmic backgrounds. We found that NGC 1068 has an excess of 79 − 20 + 22 neutrinos at tera–electron volt energies, with a global significance of 4.2σ, which we interpret as associated with the active galaxy. The flux of high-energy neutrinos that we measured from NGC 1068 is more than an order of magnitude higher than the upper limit on emissions of tera–electron volt gamma rays from this source.

Search for Unstable Sterile Neutrinos with the IceCube Neutrino Observatory

We present a search for an unstable sterile neutrino by looking for a resonant signal in eight years of atmospheric ν_{μ} data collected from 2011 to 2019 at the IceCube Neutrino Observatory. Both the (stable) three-neutrino and the 3+1 sterile neutrino models are disfavored relative to the unstable sterile neutrino model, though with p values of 2.8% and 0.81%, respectively, we do not observe evidence for 3+1 neutrinos with neutrino decay. The best-fit parameters for the sterile neutrino with decay model from this study are Δm_{41}^{2}=6.7_{-2.5}^{+3.9}  eV^{2}, sin^{2}2θ_{24}=0.33_{-0.17}^{+0.20}, and g^{2}=2.5π±1.5π, where g is the decay-mediating coupling. The preferred regions of the 3+1+decay model from short-baseline oscillation searches are excluded at 90% C.L.

Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale ν_{μ} Disappearance at IceCube

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter ε_{μτ}. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of -0.0041≤ε_{μτ}≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date.

Search for Relativistic Magnetic Monopoles with Eight Years of IceCube Data

We present an all-sky 90% confidence level upper limit on the cosmic flux of relativistic magnetic monopoles using 2886 days of IceCube data. The analysis was optimized for monopole speeds between 0.750c and 0.995c, without any explicit restriction on the monopole mass. We constrain the flux of relativistic cosmic magnetic monopoles to a level below 2.0×10^{-19}  cm^{-2} s^{-1} sr^{-1} over the majority of the targeted speed range. This result constitutes the most strict upper limit to date for magnetic monopoles with β≳0.8 and up to β∼0.995 and fills the gap between existing limits on the cosmic flux of nonrelativistic and ultrarelativistic magnetic monopoles.

Does Emotional Expression Moderate Implicit Racial Bias? Examining Bias Following Smiling and Angry Primes

Given the pervasiveness of prejudice, researchers have become increasingly interested in examining racial bias at the intersection of race and other social and perceptual categories that have the potential to disrupt these negative attitudes. Across three studies, we examined whether the emotional expression of racial exemplars would moderate implicit racial bias. We found that racial bias on the Affect Misattribution Procedure only emerged in response to angry but not smiling Black male faces in comparison to White (Study 1) or White and Asian (Study 3) male faces with similar emotional expressions. Racial bias was also found toward Asian targets (Studies 2 and 3), but not only following angry primes. These findings suggest that negative stereotypes about Black men can create a contrast effect, making racial bias toward smiling faces less likely to be expressed in the presence of angry Black male faces.

Comparing characteristics and outcomes of cancer to non-cancer patients admitted to general internal medicine (GIM)

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Background: Cancer prevalence is rising and there is a corresponding increase in hospitalizations across the cancer continuum. However, little is known about the patterns of care and outcomes of cancer inpatients as administrative data may not capture in-hospital details including investigations and medications required for characterization. Understanding how cancer inpatients are managed and their outcomes can help to optimize care delivery. Methods: We conducted a multicenter study of all patients admitted to GIM at seven hospitals (Toronto, Canada) from 2010 to 2017 where we deterministically linked administrative data with each hospital’s electronic information (pharmacy, orders, notes, laboratory/imaging and results) at the patient level. Multivariable regression models compared characteristics and outcomes between cancer and non-cancer patients for the top 5 non-cancer patient discharge diagnoses. Results: Among 230,040 hospitalizations, 15% had cancer listed as an ICD-10 comorbidity. The most common cancer disease sites were gastrointestinal (20%), lung (13%) and leukemia (11%). The most common discharge diagnoses for cancer patients were disease progression (9%), palliative care (6%), pneumonia (4%), leukemia (4%) and lung cancer (4%), while for non-cancer patients were: heart failure (5%), pneumonia (5%), stroke (5%), COPD (5%) and urinary tract infections (5%). In general, compared to non-cancer patients, cancer patients were younger (70 vs 72), had greater length of stay (LOS; 6.4 vs 4.6 days), in-hospital mortality (16% vs 5%), ICU use (12% vs 11%), 30 day re-admission rate (17% vs 10%) and were more likely to receive CTs (64% vs 52%), MRIs (14% vs 12%) and interventional procedures (22% vs 8%) (p < 0.001, all comparisons). When evaluating the top 5 non-cancer patient discharge diagnoses, results (adjusted for age, gender, Charlson comorbidity score and hospital) were similar wherein cancer patients had a higher in-hospital mortality (aOR = 2.02 p < 0.001), 30 day re-admission rate (aOR = 1.09 p = 0.08) and were more likely to receive CTs (aOR = 1.88 p < 0.001), MRIs (aOR = 1.66 p < 0.001) or interventional procedures (aOR = 1.78 p < 0.001), despite similar mean LOS (5.7 vs 5.1 days p = 0.35). Results were similar across discharge diagnoses. Conclusions: Cancer patients represent a unique population on GIM and have higher resource use, mortality and LOS compared to non-cancer patients, with similar trends even for the same non-cancer diagnoses. Specialized models of care for hospitalized cancer patients may be warranted.

eV-Scale Sterile Neutrino Search Using Eight Years of Atmospheric Muon Neutrino Data from the IceCube Neutrino Observatory

The results of a 3+1 sterile neutrino search using eight years of data from the IceCube Neutrino Observatory are presented. A total of 305 735 muon neutrino events are analyzed in reconstructed energy-zenith space to test for signatures of a matter-enhanced oscillation that would occur given a sterile neutrino state with a mass-squared differences between 0.01 and 100  eV^{2}. The best-fit point is found to be at sin^{2}(2θ_{24})=0.10 and Δm_{41}^{2}=4.5  eV^{2}, which is consistent with the no sterile neutrino hypothesis with a p value of 8.0%.

Characteristics of the Diffuse Astrophysical Electron and Tau Neutrino Flux with Six Years of IceCube High Energy Cascade Data

We report on the first measurement of the astrophysical neutrino flux using particle showers (cascades) in IceCube data from 2010-2015. Assuming standard oscillations, the astrophysical neutrinos in this dedicated cascade sample are dominated (∼90%) by electron and tau flavors. The flux, observed in the sensitive energy range from 16 TeV to 2.6 PeV, is consistent with a single power-law model as expected from Fermi-type acceleration of high energy particles at astrophysical sources. We find the flux spectral index to be γ=2.53±0.07 and a flux normalization for each neutrino flavor of ϕ_{astro}=1.66_{-0.27}^{+0.25} at E_{0}=100  TeV, in agreement with IceCube's complementary muon neutrino results and with all-neutrino flavor fit results. In the measured energy range we reject spectral indices γ≤2.28 at ≥3σ significance level. Because of high neutrino energy resolution and low atmospheric neutrino backgrounds, this analysis provides the most detailed characterization of the neutrino flux at energies below ∼100  TeV compared to previous IceCube results. Results from fits assuming more complex neutrino flux models suggest a flux softening at high energies and a flux hardening at low energies (p value ≥0.06). The sizable and smooth flux measured below ∼100  TeV remains a puzzle. In order to not violate the isotropic diffuse gamma-ray background as measured by the Fermi Large Area Telescope, it suggests the existence of astrophysical neutrino sources characterized by dense environments which are opaque to gamma rays.

Actigraphic detection of periodic limb movements: development and validation of a potential device-independent algorithm. A proof of concept study

STUDY OBJECTIVES

We propose a unique device-independent approach to analyze long-term actigraphy signals that can accurately quantify the severity of periodic limb movements in sleep (PLMS).

METHODS

We analyzed 6-8 hr of bilateral ankle actigraphy data for 166 consecutively consenting patients who simultaneously underwent routine clinical polysomnography. Using the proposed algorithm, we extracted 14 time and frequency features to identify PLMS. These features were then used to train a Naïve-Bayes learning tool which permitted classification of mild vs. severe PLMS (i.e. periodic limb movements [PLM] index less than vs. greater than 15 per hr), as well as classification for four PLM severities (i.e. PLM index < 15, between 15 and 29.9, between 30 and 49.9, and ≥50 movements per hour).

RESULTS

Using the proposed signal analysis technique, coupled with a leave-one-out cross-validation method, we obtained a classification accuracy of 89.6%, a sensitivity of 87.9%, and a specificity of 94.1% when classifying a PLM index less than vs. greater than 15 per hr. For the multiclass classification for the four PLM severities, we obtained a classification accuracy of 85.8%, with a sensitivity of 97.6%, and a specificity of 84.8%.

CONCLUSIONS

Our approach to analyzing long-term actigraphy data provides a method that can be used as a screening tool to detect PLMS using actigraphy devices from various manufacturers and will facilitate detection of PLMS in an ambulatory setting.

Time-Integrated Neutrino Source Searches with 10 Years of IceCube Data

This Letter presents the results from pointlike neutrino source searches using ten years of IceCube data collected between April 6, 2008 and July 10, 2018. We evaluate the significance of an astrophysical signal from a pointlike source looking for an excess of clustered neutrino events with energies typically above ∼1  TeV among the background of atmospheric muons and neutrinos. We perform a full-sky scan, a search within a selected source catalog, a catalog population study, and three stacked Galactic catalog searches. The most significant point in the northern hemisphere from scanning the sky is coincident with the Seyfert II galaxy NGC 1068, which was included in the source catalog search. The excess at the coordinates of NGC 1068 is inconsistent with background expectations at the level of 2.9σ after accounting for statistical trials from the entire catalog. The combination of this result along with excesses observed at the coordinates of three other sources, including TXS 0506+056, suggests that, collectively, correlations with sources in the northern catalog are inconsistent with background at 3.3σ significance. The southern catalog is consistent with background. These results, all based on searches for a cumulative neutrino signal integrated over the 10 years of available data, motivate further study of these and similar sources, including time-dependent analyses, multimessenger correlations, and the possibility of stronger evidence with coming upgrades to the detector.

Integrated Signal Encoding and Analysis System for Actigraphy-based Long-term Monitoring of Periodic Limb Movements in Sleep

There has been a boom in the development of wearable devices for wellness and healthcare applications. Numerous studies have been conducted on the utility of employing wearable devices for the long-term monitoring of biosignals. Despite their efficacy, the potential for practical implementation faces many hurdles such as memory usage, power consumption, denoising, and efficient data transmission. Of the many wearables being used, the actigraph has been a popular choice amongst experts for identifying motion abnormalities such as periodic leg movements (PLMs) in sleep and the activities of patients suffering from various medical illnesses. In this paper, we present an efficient pulse code modulation based, 3-bit, signal encoding technique, which when applied to long-term (6-8 hours), 16-bit sleep actigraphy signals, generates 3-bit encoded, accelerometry data with an average compression ratio of 92%, an average increase in the signal-to-noise (SNR) ratio by 20 dB and an average reduction of memory usage by 92%. The proposed technique also eliminates the need to apply filters for denoising, by retaining only characteristic signal information in the quantized version. The proposed technique, in general, could be applied to accelerometer-based wearables and has the potential to provide efficient memory and power usage in long-term monitoring applications.

Advanced signal analysis for the detection of periodic limb movements from bilateral ankle actigraphy

Actigraphy can assist in the detection of periodic limb movements in sleep. Although several actigraphs have been previously reported to accurately detect periodic limb movements, many are no longer available; of the existing actigraphs, most sample too infrequently to accurately detect periodic limb movements. The purpose of this study was to use advanced signal analysis to validate a readily available actigraph that has the capability of sampling at relatively high frequencies. We simultaneously recorded polysomnography and bilateral ankle actigraphy in 96 consecutive patients presenting to our sleep laboratory. After pre-processing and conditioning, the bilateral ankle actigraphy signals were then analysed for 14 simple time, frequency and morphology-based features. These features reduced the signal dimensionality and aided in better representation of the periodic limb movement activity in the actigraph signals. These features were then processed by a Naïve-Bayes binary classifier for distinguishing between normal and abnormal periodic limb movement indices. We trained the Naïve-Bayes classifier using a training set, and subsequently tested its classification accuracy using a testing set. From our experiments, using a periodic limb movement index cut-off of 5, we found that the Naïve-Bayes classifier had a correct classification rate of 78.9%, with a sensitivity of 80.3% and a specificity of 73.7%. The algorithm developed in this study has the potential of facilitating identification of periodic limb movements across a wide spectrum of patient populations via the use of bilateral ankle actigraphy.