Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning

Botulinum Toxin Type A (BoNT-A) Use for Post-Stroke Spasticity: A Multicenter Study Using Natural Language Processing and Machine Learning

We conducted a multicenter and retrospective study to describe the use of botulinum toxin type A (BoNT-A) to treat post-stroke spasticity (PSS). Data were extracted from free-text in electronic health records (EHRs) in five Spanish hospitals. We included adults diagnosed with PSS between January 2015 and December 2019, stratified into BoNT-A-treated and untreated groups. We used EHRead® technology, which incorporates natural language processing and machine learning, as well as SNOMED CT terminology. We analyzed demographic data, stroke characteristics, BoNT-A use patterns, and other treatments. We reviewed the EHRs of 1,233,929 patients and identified 2190 people with PSS with a median age of 69 years; in total, 52.1% were men, 70.7% had cardiovascular risk factors, and 63.2% had suffered an ischemic stroke. Among the PSS patients, 25.5% received BoNT-A at least once. The median time from stroke to spasticity onset was 205 days, and the time from stroke to the first BoNT-A injection was 364 days. The primary goal of BoNT-A treatment was pain control. Among the study cohort, rehabilitation was the most common non-pharmacological treatment (95.5%). Only 3.3% had recorded monitoring scales. In conclusion, a quarter of patients with PSS received BoNT-A mainly for pain relief, typically one year after the stroke. Early treatment, disease monitoring, and better data documentation in EHRs are crucial to improve PSS patients' care.

Prediction-based variable selection for component-wise gradient boosting

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting. These approaches include Akaikes Information Criterion (AIC) as well as a selection rule relying on the component-wise test error computed via cross-validation. We implemented the AIC and cross-validation routines for Generalized Linear Models and evaluated them regarding their variable selection properties and predictive performance. An extensive simulation study revealed improved selection properties whereas the prediction error could be lowered in a real world application with age-standardized COVID-19 incidence rates.

The influence of sex is a neglected focus in rotator cuff repair: A systematic review and meta-analysis

PURPOSE

Rotator cuff (RC) disorders are the most common cause of shoulder disability. The aim of this study was to quantify the evidence on the sex-related differences in RC repair.

METHODS

A systematic review of the literature was performed in January 2023 in PubMed, Wiley Cochrane Library and Web of Science on research articles on humans with RC tears treated surgically. A meta-analysis was performed to compare results in men and women. The Downs and Black score and the modified Coleman methodology score (MCMS) were used to assess the retrieved studies.

RESULTS

A total of 39,909 patients were enroled in the 401 studies analysed (45% women, 55% men). A trend toward more sex-balanced recruitment was observed over time. Only 4% of the studies on 1.5% of the documented patients presented disaggregated outcome data and were quantitatively analysed. A tendency for lower range of motion values after surgery was found for external shoulder rotation in women, with 39.9° ± 6.9° versus 45.3° ± 4.1° in men (p = 0.066). According to Downs and Black scores, four studies were good and 12 fair, with a main MCMS score of 69/100.

CONCLUSION

There is a lack of awareness on the importance to document women- and men-specific data. Only 4% of the articles disaggregated data, and it was possible to analyse only 1.5% of the literature population, a sample which cannot be considered representative of all patients. The lack of disaggregated data is alarming and calls for action to better study men and women differences to optimise the management of RC tears. This will be necessary to provide sex-specific information that could be of clinical relevance when managing RC repair.

LEVEL OF EVIDENCE

Level IV.

Chronic liver disease-associated severe thrombocytopenia in Spain: Results from a retrospective study using machine learning and natural language processing

BACKGROUND

Patients with chronic liver disease (CLD) often develop thrombocytopenia (TCP) as a complication. Severe TCP (platelet count<50×109/L) can increase morbidity and complicate CLD management, increasing bleeding risk during invasive procedures.

OBJECTIVES

To describe the real-world scenario of CLD-associated severe TCP patients' clinical characteristics. To evaluate the association between invasive procedures, prophylactic treatments, and bleeding events in this group of patients. To describe their need of medical resource use in Spain.

METHODS

This is a retrospective, multicenter study including patients who had confirmed diagnosis of CLD and severe TCP in four hospitals within the Spanish National Healthcare Network from January 2014 to December 2018. We analyzed the free-text information from Electronic Health Records (EHRs) of patients using Natural Language Processing (NLP), machine learning techniques, and SNOMED-CT terminology. Demographics, comorbidities, analytical parameters and characteristics of CLD were extracted at baseline and need for invasive procedures, prophylactic treatments, bleeding events and medical resources used in the follow up period. Frequency tables were generated for categorical variables, whereas continuous variables were described in summary tables as mean (SD) and median (Q1-Q3).

RESULTS

Out of 1,765,675 patients, 1787 had CLD and severe TCP; 65.2% were male with a mean age of 54.7 years old. Cirrhosis was detected in 46% (n=820) of patients and 9.1% (n=163) had hepatocellular carcinoma. Invasive procedures were needed in 85.6% of patients during the follow up period. Patients undergoing procedures compared to those patients without invasive procedures presented higher rates of bleeding events (33% vs 8%, p<0.0001) and higher number of bleedings. While prophylactic platelet transfusions were given to 25.6% of patients undergoing procedures, TPO receptor agonist use was only detected in 3.1% of them. Most patients (60.9%) required at least one hospital admission during the follow up and 14.4% of admissions were due to bleeding events with a hospital length of stay of 6 (3, 9) days.

CONCLUSIONS

NLP and machine learning are useful tools to describe real-world data in patients with CLD and severe TCP in Spain. Bleeding events are frequent in those patients who need invasive procedures, even receiving platelet transfusions as a prophylactic treatment, increasing the further use of medical resources. Because that, new prophylactic treatments that are not yet generalized, are needed.

[Response to the COVID-19 Health Crisis from a Gender Perspective: Lessons Learned]

OBJECTIVE

To delve deeper from a gender perspective into the lessons learned during the COVID-19 pandemic to address future health crises.

METHOD

Study with key informants with experience in public health and gender from the Ministerio de Sanidad, ministries of the autonomous communities, Institut Català de la Salut, Hospital de La Princesa, Escuela Andaluza de Salud Pública and Universidad País Vasco.

SOURCE OF INFORMATION

individual open-ended questionnaire on health and health inequalities/gender inequalities related to COVID-19. After presenting the findings, the key informants group discussed them in a meeting until reaching a consensus on the lessons learned.

RESULTS

The lack of clinical statistics by sex could compromise epidemiological surveillance, losing the opportunity to characterize the disease. The performance of essential services fell more on women, exhausting them with double and triple shifts; with the differences according to sex in the clinical presentation of COVID-19, and the criteria for hospitalization/admission to the intensive care unit, their access to health care decreased. Increased: gender violence and mental health problems; delaying recognition of the second effects of vaccines in women; partially due to information biases in clinical trials. The gender perspective was lacking in academic, healthcare, and health management areas.

CONCLUSIONS

Women's gender dimensions determined their higher frequency of COVID-19 and played a fundamental role in its control. Broadly considering the lessons learned will strengthen prevention systems and be able to provide effective responses to future health crises.

Prevalence and clinical characteristics of patients with rheumatoid arthritis with interstitial lung disease using unstructured healthcare data and machine learning

OBJECTIVES

Real-world data regarding rheumatoid arthritis (RA) and its association with interstitial lung disease (ILD) is still scarce. This study aimed to estimate the prevalence of RA and ILD in patients with RA (RAILD) in Spain, and to compare clinical characteristics of patients with RA with and without ILD using natural language processing (NLP) on electronic health records (EHR).

METHODS

Observational case-control, retrospective and multicentre study based on the secondary use of unstructured clinical data from patients with adult RA and RAILD from nine hospitals between 2014 and 2019. NLP was used to extract unstructured clinical information from EHR and standardise it into a SNOMED-CT terminology. Prevalence of RA and RAILD were calculated, and a descriptive analysis was performed. Characteristics between patients with RAILD and RA patients without ILD (RAnonILD) were compared.

RESULTS

From a source population of 3 176 165 patients and 64 241 683 EHRs, 13 958 patients with RA were identified. Of those, 5.1% patients additionally had ILD (RAILD). The overall age-adjusted prevalence of RA and RAILD were 0.53% and 0.02%, respectively. The most common ILD subtype was usual interstitial pneumonia (29.3%). When comparing RAILD versus RAnonILD patients, RAILD patients were older and had more comorbidities, notably concerning infections (33.6% vs 16.5%, p<0.001), malignancies (15.9% vs 8.5%, p<0.001) and cardiovascular disease (25.8% vs 13.9%, p<0.001) than RAnonILD. RAILD patients also had higher inflammatory burden reflected in more pharmacological prescriptions and higher inflammatory parameters and presented a higher in-hospital mortality with a higher risk of death (HR 2.32; 95% CI 1.59 to 2.81, p<0.001).

CONCLUSIONS

We found an estimated age-adjusted prevalence of RA and RAILD by analysing real-world data through NLP. RAILD patients were more vulnerable at the time of inclusion with higher comorbidity and inflammatory burden than RAnonILD, which correlated with higher mortality.

Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study

Background

Spondyloarthritis (SpA) is a group of related but phenotypically distinct inflammatory disorders that include axial SpA (axSpA) and psoriatic arthritis (PsA). Information on the characteristics and management of these patients in the real world remains scarce.

Objectives

To explore the characteristics and management [disease activity assessment and treatment with secukinumab (SEC) or other biologic disease-modifying antirheumatic drugs (bDMARDs)] of axSpA and PsA patients using natural language processing (NLP) in Electronic Health Records (EHRs).

Design

National, multicenter, observational, and retrospective study.

Methods

We analyzed free-text and structured clinical information from EHR at three hospitals. All adult patients with axSpA, PsA or non-classified SpA from 2018 to 2021 with minimum follow-up of three months were included when starting SEC or other bDMARDs. Clinical variables were extracted using EHRead® technology based on Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) terminology.

Results

Out of 887,735 patients, 758 were included, of which 328 had axSpA [58.5% male; mean (SD) age of 50.7 (12.7) years], 365 PsA [54.8% female, 53.9 (12.4) years], and 65 non-classified SpA. Mean (SD) time since diagnosis was 36.8 (61.0) and 24.1 (35.2) months for axSpA and PsA, respectively. Only 116 axSpA patients (35.3%) had available Ankylosing Spondylitis Disease Activity Score (ASDAS) or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) at bDMARD onset, of which 61 presented active disease. Disease Activity in PSoriatic Arthritis (DAPSA) or Disease Assessment Score - 28 joints (DAS-28) values at bDMARD onset were available for only 61 PsA (16.7%) patients, with 23 of them having active disease. The number of patients with available tender joint count or swollen joint count assessment was 68 (20.7%) and 59 (18%) for axSpA, and 115 (31.5%) and 119 (32.6%) for PsA, respectively. SEC was used in 63 (19.2%) axSpA patients and in 63 (17.3%) PsA patients.

Conclusion

Using NLP, the study showed that around one-third of axSpA and one-sixth of PsA patients have disease activity assessments with ASDAS/BASDAI or DAPSA/DAS-28, respectively, highlighting an area of improvement in these patients' management.

Adjunctive Diagnostic Studies Completed Following Detection of Candidemia in Children: Secondary Analysis of Observed Practice From a Multicenter Cohort Study Conducted by the Pediatric Fungal Network

BACKGROUND

Adjunctive diagnostic studies (aDS) are recommended to identify occult dissemination in patients with candidemia. Patterns of evaluation with aDS across pediatric settings are unknown.

METHODS

Candidemia episodes were included in a secondary analysis of a multicenter comparative effectiveness study that prospectively enrolled participants age 120 days to 17 years with invasive candidiasis (predominantly candidemia) from 2014 to 2017. Ophthalmologic examination (OE), abdominal imaging (AbdImg), echocardiogram, neuroimaging, and lumbar puncture (LP) were performed per clinician discretion. Adjunctive diagnostic studies performance and positive results were determined per episode, within 30 days from candidemia onset. Associations of aDS performance with episode characteristics were evaluated via mixed-effects logistic regression.

RESULTS

In 662 pediatric candidemia episodes, 490 (74%) underwent AbdImg, 450 (68%) OE, 426 (64%) echocardiogram, 160 (24%) neuroimaging, and 76 (11%) LP; performance of each aDS per episode varied across sites up to 16-fold. Longer durations of candidemia were associated with undergoing OE, AbdImg, and echocardiogram. Immunocompromised status (58% of episodes) was associated with undergoing AbdImg (adjusted odds ratio [aOR] 2.38; 95% confidence intervals [95% CI] 1.51-3.74). Intensive care at candidemia onset (30% of episodes) was associated with undergoing echocardiogram (aOR 2.42; 95% CI 1.51-3.88). Among evaluated episodes, positive OE was reported in 15 (3%), AbdImg in 30 (6%), echocardiogram in 14 (3%), neuroimaging in 9 (6%), and LP in 3 (4%).

CONCLUSIONS

Our findings show heterogeneity in practice, with some clinicians performing aDS selectively, potentially influenced by clinical factors. The low frequency of positive results suggests that targeted application of aDS is warranted.

Real-World Evidence on the Clinical Characteristics and Management of Patients with Chronic Lymphocytic Leukemia in Spain Using Natural Language Processing: The SRealCLL Study

The SRealCLL study aimed to obtain real-world evidence on the clinical characteristics and treatment patterns of patients with chronic lymphocytic leukemia (CLL) using natural language processing (NLP). Electronic health records (EHRs) from seven Spanish hospitals (January 2016-December 2018) were analyzed using EHRead® technology, based on NLP and machine learning. A total of 534 CLL patients were assessed. No treatment was detected in 270 (50.6%) patients (watch-and-wait, W&W). First-line (1L) treatment was identified in 230 (43.1%) patients and relapsed/refractory (2L) treatment was identified in 58 (10.9%). The median age ranged from 71 to 75 years, with a uniform male predominance (54.8-63.8%). The main comorbidities included hypertension (W&W: 35.6%; 1L: 38.3%; 2L: 39.7%), diabetes mellitus (W&W: 24.4%; 1L: 24.3%; 2L: 31%), cardiac arrhythmia (W&W: 16.7%; 1L: 17.8%; 2L: 17.2%), heart failure (W&W 16.3%, 1L 17.4%, 2L 17.2%), and dyslipidemia (W&W: 13.7%; 1L: 18.7%; 2L: 19.0%). The most common antineoplastic treatment was ibrutinib in 1L (64.8%) and 2L (62.1%), followed by bendamustine + rituximab (12.6%), obinutuzumab + chlorambucil (5.2%), rituximab + chlorambucil (4.8%), and idelalisib + rituximab (3.9%) in 1L and venetoclax (15.5%), idelalisib + rituximab (6.9%), bendamustine + rituximab (3.5%), and venetoclax + rituximab (3.5%) in 2L. This study expands the information available on patients with CLL in Spain, describing the diversity in patient characteristics and therapeutic approaches in clinical practice.

Impact of Advanced Age on the Incidence of Major Adverse Cardiovascular Events in Patients with Type 2 Diabetes Mellitus and Stable Coronary Artery Disease in a Real-World Setting in Spain

Patients with type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) without myocardial infarction (MI) or stroke are at high risk for major cardiovascular events (MACEs). We aimed to provide real-world data on age-related clinical characteristics, treatment management, and incidence of major cardiovascular outcomes in T2DM-CAD patients in Spain from 2014 to 2018. We used EHRead® technology, which is based on natural language processing and machine learning, to extract unstructured clinical information from electronic health records (EHRs) from 12 hospitals. Of the 4072 included patients, 30.9% were younger than 65 years (66.3% male), 34.2% were aged 65-75 years (66.4% male), and 34.8% were older than 75 years (54.3% male). These older patients were more likely to have hypertension (OR 2.85), angina (OR 1.64), heart valve disease (OR 2.13), or peripheral vascular disease (OR 2.38) than those aged <65 years (p < 0.001 for all comparisons). In general, they were also more likely to receive pharmacological and interventional treatments. Moreover, these patients had a significantly higher risk of MACEs (HR 1.29; p = 0.003) and ischemic stroke (HR 2.39; p < 0.001). In summary, patients with T2DM-CAD in routine clinical practice tend to be older, have more comorbidities, are more heavily treated, and have a higher risk of developing MACE than is commonly assumed from clinical trial data.

Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning

INTRODUCTION

Patients with cancer and venous thromboembolism (VTE) show a high risk of VTE recurrence during anticoagulant treatment. This study aimed to develop a predictive model to assess the risk of VTE recurrence within 6 months at the moment of primary VTE diagnosis in these patients.

MATERIALS AND METHODS

Using the EHRead® technology, based on Natural Language Processing (NLP) and machine learning (ML), the unstructured data in electronic health records from 9 Spanish hospitals between 2014 and 2018 were extracted. Both clinically- and ML-driven feature selection were performed to identify predictors for VTE recurrence. Logistic regression (LR), decision tree (DT), and random forest (RF) algorithms were used to train different prediction models, which were subsequently validated in a hold-out data set.

RESULTS

A total of 16,407 anticoagulated cancer patients with diagnosis of VTE were identified (54.4 % male and median age 70). Deep vein thrombosis, pulmonary embolism and metastases were observed in 67.2 %, 26.6 %, and 47.7 % of the patients, respectively. During the study follow-up, 11.4 % of the patients developed a recurrent VTE, being more frequent in patients with lung cancer. Feature selection and model training based on ML identified primary pulmonary embolism, deep vein thrombosis, metastasis, adenocarcinoma, hemoglobin and serum creatinine levels, platelet and leukocyte count, family history of VTE, and patients' age as predictors of VTE recurrence within 6 months of VTE diagnosis. The LR model had an AUC-ROC (95 % CI) of 0.66 (0.61, 0.70), the DT of 0.69 (0.65, 0.72) and the RF of 0.68 (0.63, 0.72).

CONCLUSIONS

This is the first ML-based predictive model designed to predict 6-months VTE recurrence in patients with cancer. These results hold great potential to assist clinicians to identify the high-risk patients and improve their clinical management.

Association between laboratory markers and Covid-19 disease severity and outcome: a retrospective cohort study in Saudi Arabia

Introduction

In Saudi Arabia, limited studies have evaluated factors including epidemiologic, clinical, and laboratory findings that are associated with COVID-19 disease. The aim of this paper was to identify laboratory parameters used in King Abdulaziz University Hospital which show an association with disease severity and patient outcome in the form of mortality.

Methods

Age, gender, medical history, and laboratory parameters were all retrospectively assessed concerning disease severity and disease outcome in a total of 111 COVID-19 patients at King Abdulaziz University Hospital between July 2020 and August 2020. Patients were categorized into mild disease if they did not require ward admission, moderate if they met the Ministry of Health criteria for isolation ward admition, and severe if they were admitted to the ICU.

Results

Age but not gender was associated with the disease severity X² (4, N = 110) = 27.2, p <0.001. Of all laboratory parameters on admission, only the levels of Albumin appeared to be significantly associated X² (2, N =70) = 6.6, p <0.05 with disease severity. Age but not gender was also significantly associated with disease outcome X² (2, N = 110) = 12.8, p < 0.01. Interestingly, RBC count also showed a significant relation with disease outcome X² (2, N = 71) = 6.1, p <0.05.

Discussion

This study provides more understanding of the laboratory characteristics in our part of the world to efficiently manage the disease.

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to those of models implementing only tabular data. For 30-day mortality, the combined model yielded slightly better performances than the tabular fastai and XGBoost models, with AUC 0.87 ± 0.02, F1 score 0.62 ± 0.10 and an MCC 0.52 ± 0.04 (p < 0.32). As for ICU admission, the combined model MCC was superior (p < 0.024) to the tabular models. Our results suggest that a combined textual and tabular model can effectively predict COVID-19 prognosis which may assist ED physicians in their decision-making process.

The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review

Background

Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care.

Methods

We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data.

Results

Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health.

Conclusions

Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.

Females have a different metabolic response to critical illness, measured by comprehensive amino acid flux analysis

BACKGROUND

The trajectory from healthy to critical illness is influenced by numerous factors, including metabolism, which differs substantially between males and females. Whole body protein breakdown is substantially increased in critically ill patients, but it remains unclear whether there are sex differences that could explain the different health outcomes. Hence, we performed a secondary analysis of a study, where we used a novel pulse isotope method in critically ill and matched healthy males and females.

METHODS

In 51 critically ill ICU patients (26 males, 15 females) and 49 healthy controls (36 males and 27 females), we assessed their general and disease characteristics and collected arterial(ized) blood in the postabsorptive state after pulse administration of 8 ml of a solution containing 18 stable AA tracers. In contrast to the original study, we now fitted the decay curves and calculated non-compartmental whole body amino acid production (WBP) and compartmental measurements of metabolism, including intracellular amino acid production. We measured amino acid enrichments and concentrations by LC-MS/MS and derived statistics using AN(C)OVA.

RESULTS

Critically ill males and females showed an increase in the WBP of many amino acids, including those related to protein breakdown, but females showed greater elevations, or in the event of a reduction, attenuated reductions. Protein breakdown-independent WBP differences remained between males and females, notably increased glutamine and glutamate WBP. Only severely ill females showed a lower increase in WBP of many amino acids in comparison to moderately ill females, suggesting a suppressed metabolism. Compartmental analysis supported the observations.

CONCLUSIONS

The present study shows that females have a different response to critical illness in the production of several amino acids and changes in protein breakdown, observations made possible using our innovative stable tracer pulse approach.

CLINICAL TRIAL REGISTRY

Data are from the baseline measurements of study NCT02770092 (URL: https://clinicaltrials.gov/ct2/show/NCT02770092) and NCT03628365 (URL: https://clinicaltrials.gov/ct2/show/NCT03628365).

Sex differences in COVID-19 symptoms and outcomes in people with kidney failure treated with dialysis: a prospective cohort study

BACKGROUND

People with kidney failure treated with dialysis are at increased risk of SARS-CoV-2 infection, and severe COVID-19 outcomes such as hospitalization and death. Though there are well-defined sex differences in outcomes for the general population with COVID-19, we do not know whether this translates into kidney failure populations. We aimed to estimate the differences in COVID-19 symptoms and clinical outcomes between males and females treated with maintenance dialysis.

METHODS

In this prospective observational cohort study, we included adults treated with maintenance dialysis in Southern Alberta, Canada that tested positive for COVID-19 between March 2020 and February 2022. We examined the association between sex (dichotomized as male and female) with COVID-19 symptoms including fever, cough, malaise, shortness of breath, muscle joints/aches, nausea and/or vomiting, loss of appetite, diarrhea, headache, sore throat, and loss of smell/taste using chi-square or Fisher's exact tests. Secondary outcomes included 30-day hospitalization, ICU admission, and death.

RESULTS

Of 1,329 cohort participants, 246 (18.5%) tested positive for SARS-CoV-2 and were included in our study, including 95 females (39%). Of 207 participants with symptoms assessed, females had less frequent fever (p = 0.003), and more nausea or vomiting (p = 0.003) compared to males, after correction for multiple testing. Males exhibited no symptoms 25% of the time, compared with 10% of females (p = 0.01, not significant when corrected for multiple testing). We did not identify statistically significant differences in clinical outcomes between the sexes, though vaccinated patients had lower odds of hospitalization.

CONCLUSIONS

Sex differences in COVID-19 symptoms were identified in a cohort of patients treated with maintenance dialysis, which may inform sex-specific screening strategies in dialysis units. Further work is necessary to examine mechanisms for identified sex differences.

Sex differences in D-dimer and critical illness in patients with COVID-19: A systematic review and meta-analysis

Background

Observed sex differences in COVID-19 outcomes suggest that men are more likely to experience critical illness and mortality. Thrombosis is common in severe COVID-19, and D-dimer is a significant marker for COVID-19 severity and mortality. It is unclear whether D-dimer levels differ between men and women, and the effect of D-dimer levels on disease outcomes remains under investigation.

Objectives

We aimed to evaluate the sex difference in the D-dimer level among hospitalized patients with COVID-19 and the effect of sex and D-dimer level on disease outcomes.

Methods

We meta-analyzed articles reporting D-dimer levels in men and women hospitalized for COVID-19, until October 2021, using random effects. Primary outcomes were mortality, critical illness, and thrombotic complications.

Results

In total, 11,682 patients from 10 studies were analyzed (N = 5606 men (55.7%), N = 5176 women (44.3%)). Men had significantly higher odds of experiencing mortality (odds ratios (OR) = 1.41, 95% CI: [1.25, 1.59], P ≤ .001, I² = 0%) and critical illness (OR = 1.76, 95% CI: [1.43, 2.18], P ≤ .001, I² = 61%). The mean D-dimer level was not significantly different between men and women (MD = 0.08, 95% CI: [-0.23, 0.40], P = .61, I² = 52%). In the subgroup analysis, men had significantly higher odds of experiencing critical illness compared with women in both the "higher" (P = .006) and "lower" (P = .001) D-dimer subgroups.

Conclusion

Men have significantly increased odds of experiencing poor COVID-19 outcomes compared with women. No sex difference was found in the D-dimer level between men and women with COVID-19. The diversity in D-dimer reporting impacts data interpretation and requires further attention.

Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

Natural Language Processing for Improved Characterization of COVID-19 Symptoms: Observational Study of 350,000 Patients in a Large Integrated Health Care System

BACKGROUND

Natural language processing (NLP) of unstructured text from electronic medical records (EMR) can improve the characterization of COVID-19 signs and symptoms, but large-scale studies demonstrating the real-world application and validation of NLP for this purpose are limited.

OBJECTIVE

The aim of this paper is to assess the contribution of NLP when identifying COVID-19 signs and symptoms from EMR.

METHODS

This study was conducted in Kaiser Permanente Southern California, a large integrated health care system using data from all patients with positive SARS-CoV-2 laboratory tests from March 2020 to May 2021. An NLP algorithm was developed to extract free text from EMR on 12 established signs and symptoms of COVID-19, including fever, cough, headache, fatigue, dyspnea, chills, sore throat, myalgia, anosmia, diarrhea, vomiting or nausea, and abdominal pain. The proportion of patients reporting each symptom and the corresponding onset dates were described before and after supplementing structured EMR data with NLP-extracted signs and symptoms. A random sample of 100 chart-reviewed and adjudicated SARS-CoV-2-positive cases were used to validate the algorithm performance.

RESULTS

A total of 359,938 patients (mean age 40.4 [SD 19.2] years; 191,630/359,938, 53% female) with confirmed SARS-CoV-2 infection were identified over the study period. The most common signs and symptoms identified through NLP-supplemented analyses were cough (220,631/359,938, 61%), fever (185,618/359,938, 52%), myalgia (153,042/359,938, 43%), and headache (144,705/359,938, 40%). The NLP algorithm identified an additional 55,568 (15%) symptomatic cases that were previously defined as asymptomatic using structured data alone. The proportion of additional cases with each selected symptom identified in NLP-supplemented analysis varied across the selected symptoms, from 29% (63,742/220,631) of all records for cough to 64% (38,884/60,865) of all records with nausea or vomiting. Of the 295,305 symptomatic patients, the median time from symptom onset to testing was 3 days using structured data alone, whereas the NLP algorithm identified signs or symptoms approximately 1 day earlier. When validated against chart-reviewed cases, the NLP algorithm successfully identified signs and symptoms with consistently high sensitivity (ranging from 87% to 100%) and specificity (94% to 100%).

CONCLUSIONS

These findings demonstrate that NLP can identify and characterize a broad set of COVID-19 signs and symptoms from unstructured EMR data with enhanced detail and timeliness compared with structured data alone.

Clinical Characterization of Inpatients with Acute Conjunctivitis: A Retrospective Analysis by Natural Language Processing and Machine Learning

Background Acute bacterial conjunctivitis (ABC) is a relatively common medical condition caused by different pathogens. Although it rarely threatens vision, it is one of the most common conditions that cause red eyes and may be accompanied by discomfort and discharge. The study aimed to identify and characterize inpatients with ABC treated with topical antibiotics. Methods The EHRead® technology, based on natural language processing (NLP) and machine learning, was used to extract and analyze the clinical information in the electronic health records (EHRs) of antibiotic-treated patients with conjunctivitis and admitted to five hospitals in Spain between January 2014 and December 2018. Categorical variables were described by frequency, whereas numerical variables included the mean, standard deviation, median, and quartiles. Results From a source population of 2,071,812 adult patients who attended the participating hospitals in the study period, 11,110 patients diagnosed with acute conjunctivitis were identified. Six thousand five hundred eighty-three patients were treated with antibiotics, comprising the final study population. Microbiology was tested only on 12.1% of patients. Antibiotics, mainly tobramycin, and corticosteroids, mainly dexamethasone, were usually prescribed. NSAIDs were also used in about 50% of patients, always combined with antibiotics. Conclusions The present study provided a realistic representation of the hospital practice concerning managing patients with acute antibiotic-treated conjunctivitis. The diagnosis is usually based on the clinical ground, microbiology is rarely tested, few bacteria species are involved, and local antibiotics are frequently associated with corticosteroids and/or NSAIDs. Moreover, this study provided clinically relevant outcomes, based on new technology, that could be applied in clinical practice.

Sex-differences in COVID-19 diagnosis, risk factors and disease comorbidities: A large US-based cohort study

Introduction

Morbidity and mortality from COVID-19 are higher among men, however, underlying pathways remain controversial. We aim to investigate sex-gender differences in COVID-19 in a large US-based cohort, namely COVID-19 Research Database. More specifically, the objectives are to explore the socio-economic characteristics of COVID-19 male and female patients and to examine potential sex differences in lifestyle factors and disease comorbidities among diagnosed patients.

Methods

This is a retrospective cohort study contrasting male vs. female patients with test-confirmed COVID-19. The study used Healthjump electronic medical records (e.g., demographics, encounters, medical history, and vitals) extracted from January 2020 to December 2021 (N = 62,310).

Results

Significant sociodemographic and comorbidity differences were observed between males and females (p < 0.05). For example, a significantly higher proportion of males (vs. females) were aged ≥70-year-old (17.04 vs. 15.01%) and smokers (11.04 vs. 9.24%, p < 0.0001). In addition, multiple logistic regression showed that hypertension and diabetes were significantly more frequent in males [adjusted odds ratio (ORa) = 66.19 and ORa = 22.90].

Conclusions

Understanding the differences in outcomes between male and female patients will inform gender equity responsive approach to COVID-19 and enhance the effectiveness of clinical practice, health policy and interventions.

Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing

Patients with Type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are at high risk of developing major adverse cardiovascular events (MACE). This is a multicenter, retrospective, and observational study performed in Spain aimed to characterize these patients in a real-world setting. Unstructured data from the Electronic Health Records were extracted by EHRead^®, a technology based on Natural Language Processing and machine learning. The association between new MACE and the variables of interest were investigated by univariable and multivariable analyses. From a source population of 2,184,662 patients, we identified 4072 adults diagnosed with T2DM and CAD (62.2% male, mean age 70 ± 11). The main comorbidities observed included arterial hypertension, hyperlipidemia, and obesity, with metformin and statins being the treatments most frequently prescribed. MACE development was associated with multivessel (Hazard Ratio (HR) = 2.49) and single coronary vessel disease (HR = 1.71), transient ischemic attack (HR = 2.01), heart failure (HR = 1.32), insulin treatment (HR = 1.40), and percutaneous coronary intervention (PCI) (HR = 2.27), whilst statins (HR = 0.73) were associated with a lower risk of MACE occurrence. In conclusion, we found six risk factors associated with the development of MACE which were related with cardiovascular diseases and T2DM severity, and treatment with statins was identified as a protective factor for new MACE in this study.

Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records

False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) "women," "men," or "sex"; (b) "big data," "artificial intelligence," or "NLP"; and (c) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in healthresearch, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases.

Gender perspective in COVID-19. SESPAS Report 2022

We failed to adequately launch a gender transformative response to COVID-19 pandemic, data by sex on a variety of indicators for most countries are hard to find. Some symptoms reported as common of COVID-19 infection, are more prominent in men, while others are more prominent in women, one cannot with certainty exclude that some of the differences observed could be due to gender bias in the management of cases in health services. The gender implications of the pandemic reach wide and far. Inequalities can be further aggravated as sex and gender intersect with other axes of inequality. The SAGER guidelines exemplify an effort to improve reporting of sex and gender dimensions and encouraging researchers to integrate these aspects in the research design. these observations and emerging evidence about the persistent gender-blind approach to COVID-19 is a wake-up call to change course. National Gender Equality Institutions can be central in ensuring gender matters are considered in government responses. COVID-19 pandemic is an opportunity to reverse the trend and take action to apply an intersectional feminist approach to global health that enables a just and equal world where everyone's health and wellbeing matter.

Understanding the COVID-19 Pandemic in Nursing Homes (Aragón, Spain): Sociodemographic and Clinical Factors Associated With Hospitalization and Mortality

Old people residing in nursing homes have been a vulnerable group to the coronavirus disease 2019 (COVID-19) pandemic, with high rates of infection and death. Our objective was to describe the profile of institutionalized patients with a confirmed COVID-19 infection and the socioeconomic and morbidity factors associated with hospitalization and death. We conducted a retrospective cohort study including data from subjects aged 65 years or older residing in a nursing home with a confirmed COVID-19 infection from March 2020 to March 2021 (4,632 individuals) in Aragón (Spain). We analyzed their sociodemographic and clinical profiles and factors related to hospitalization and mortality at 7, 30, and 90 days of COVID-19 diagnosis using logistic regression analyses. We found that the risk of hospitalization and mortality varied according to sociodemographic and morbidity profile. There were inequalities in hospitalization by socioeconomic status and gender. Patients with low contributory pensions and women had a lower risk of hospitalization. Diabetes mellitus, heart failure, and chronic kidney disease were associated with a higher risk of hospitalization. On the contrary, people with dementia showed the highest risk of mortality with no hospitalization. Patient-specific factors must be considered to develop equitable and effective measures in nursing homes to be prepared for future health threats.

Detection of SARS-CoV-2 infection by exhaled breath spectral analysis: Introducing a ready-to-use point-of-care mass screening method

BACKGROUND

The current SARS-CoV-2 pandemic created an urgent need for rapid, infection screening applied to large numbers of asymptomatic individuals. To date, nasal/throat swab polymerase chain reaction (PCR) is considered the "gold standard". However, this is inconducive to mass, point-of-care (POC) testing due to person discomfort during sampling and a prolonged result turnaround. Breath testing for disease specific organic compounds potentially offers a practical, rapid, non-invasive, POC solution. The study compares the Breath of Health, Ltd. (BOH) breath analysis system to PCR's ability to screen asymptomatic individuals for SARS-CoV-2 infection. The BOH system is mobile and combines Fourier-transform infrared (FTIR) spectroscopy with artificial intelligence (AI) to generate results within 2 min and 15 s. In contrast to prior SARS-CoV-2 breath analysis research, this study focuses on diagnosing SARS-CoV-2 via disease specific spectrometric profiles rather than through identifying the disease specific molecules.

METHODS

Asymptomatic emergency room patients with suspected SARS-CoV-2 exposure in two leading Israeli hospitals were selected between February through April 2021. All were tested via nasal/throat-swab PCR and BOH breath analysis. In total, 297 patients were sampled (mean age 57·08 ± SD 18·86, 156 males, 139 females, 2 unknowns). Of these, 96 were PCR-positive (44 males, 50 females, 2 unknowns), 201 were PCR-negative (112 males, 89 females). One hundred samples were used for AI identification of SARS-CoV-2 distinguishing spectroscopic wave-number patterns and diagnostic algorithm creation. Algorithm validation was tested in 100 proof-of-concept samples (34 PCR-positive, 66 PCR-negative) by comparing PCR with AI algorithm-based breath-test results determined by a blinded medical expert. One hundred additional samples (12 true PCR-positive, 85 true PCR-negative, 3 confounder false PCR-positive [not included in the 297 total samples]) were evaluated by two blinded medical experts for further algorithm validation and inter-expert correlation.

FINDINGS

The BOH system identified three distinguishing wave numbers for SARS-CoV-2 infection. In the first phase, the single expert identified the first 100 samples correctly, yielding a 1:1 FTIR/AI:PCR correlation. The two-expert second-phase also yielded 1:1 FTIR/AI:PCR correlation for 97 non-confounders and null correlation for the 3 confounders. Inter-expert correlation was 1:1 for all results. In total, the FTIR/AI algorithm demonstrated 100% sensitivity and specificity for SARS-CoV-2 detection when compared with PCR.

INTERPRETATION

The SARS-CoV-2 method of breath analysis via FTIR with AI-based algorithm demonstrated high PCR correlation in screening for asymptomatic individuals. This is the first practical, rapid, POC breath analysis solution with such high PCR correlation in asymptomatic individuals. Further validation is required with a larger sample size.

FUNDING

Breath of Health Ltd, Rehovot, Israel provided study funding.

Assessment of medical management in Coronary Type 2 Diabetic patients with previous percutaneous coronary intervention in Spain: A retrospective analysis of electronic health records using Natural Language Processing

INTRODUCTION AND OBJECTIVES

Patients with type 2 diabetes (T2D) and stable coronary artery disease (CAD) previously revascularized with percutaneous coronary intervention (PCI) are at high risk of recurrent ischemic events. We aimed to provide real-world insights into the clinical characteristics and management of this clinical population, excluding patients with a history of myocardial infarction (MI) or stroke, using Natural Language Processing (NLP) technology.

METHODS

This is a multicenter, retrospective study based on the secondary use of 2014-2018 real-world data captured in the Electronic Health Records (EHRs) of 1,579 patients (0.72% of the T2D population analyzed; n = 217,632 patients) from 12 representative hospitals in Spain. To access the unstructured clinical information in EHRs, we used the EHRead® technology, based on NLP and machine learning. Major adverse cardiovascular events (MACE) were considered: MI, ischemic stroke, urgent coronary revascularization, and hospitalization due to unstable angina. The association between MACE rates and the variables included in this study was evaluated following univariate and multivariate approaches.

RESULTS

Most patients were male (72.13%), with a mean age of 70.5±10 years. Regarding T2D, most patients were non-insulin-dependent T2D (61.75%) with high prevalence of comorbidities. The median (Q1-Q3) duration of follow-up was 1.2 (0.3-4.5) years. Overall, 35.66% of patients suffered from at least one MACE during follow up. Using a Cox Proportional Hazards regression model analysis, several independent factors were associated with MACE during follow up: CAD duration (p < 0.001), COPD/Asthma (p = 0.021), heart valve disease (p = 0.031), multivessel disease (p = 0.005), insulin treatment (p < 0.001), statins treatment (p < 0.001), and clopidogrel treatment (p = 0.039).

CONCLUSIONS

Our results showed high rates of MACE in a large real-world series of PCI-revascularized patients with T2D and CAD with no history of MI or stroke. These data represent a potential opportunity to improve the clinical management of these patients.

Which children and young people are at higher risk of severe disease and death after hospitalisation with SARS-CoV-2 infection in children and young people: A systematic review and individual patient meta-analysis

BACKGROUND

We aimed to describe pre-existing factors associated with severe disease, primarily admission to critical care, and death secondary to SARS-CoV-2 infection in hospitalised children and young people (CYP), within a systematic review and individual patient meta-analysis.

METHODS

We searched Pubmed, European PMC, Medline and Embase for case series and cohort studies published between 1st January 2020 and 21st May 2021 which included all CYP admitted to hospital with ≥ 30 CYP with SARS-CoV-2 or ≥ 5 CYP with PIMS-TS or MIS-C. Eligible studies contained (1) details of age, sex, ethnicity or co-morbidities, and (2) an outcome which included admission to critical care, mechanical invasive ventilation, cardiovascular support, or death. Studies reporting outcomes in more restricted groupings of co-morbidities were eligible for narrative review. We used random effects meta-analyses for aggregate study-level data and multilevel mixed effect models for IPD data to examine risk factors (age, sex, comorbidities) associated with admission to critical care and death. Data shown are odds ratios and 95% confidence intervals (CI).PROSPERO: CRD42021235338.

FINDINGS

83 studies were included, 57 (21,549 patients) in the meta-analysis (of which 22 provided IPD) and 26 in the narrative synthesis. Most studies had an element of bias in their design or reporting. Sex was not associated with critical care or death. Compared with CYP aged 1-4 years (reference group), infants (aged <1 year) had increased odds of admission to critical care (OR 1.63 (95% CI 1.40-1.90)) and death (OR 2.08 (1.57-2.86)). Odds of death were increased amongst CYP over 10 years (10-14 years OR 2.15 (1.54-2.98); >14 years OR 2.15 (1.61-2.88)).The number of comorbid conditions was associated with increased odds of admission to critical care and death for COVID-19 in a step-wise fashion. Compared with CYP without comorbidity, odds ratios for critical care admission were: 1.49 (1.45-1.53) for 1 comorbidity; 2.58 (2.41-2.75) for 2 comorbidities; 2.97 (2.04-4.32) for ≥3 comorbidities. Corresponding odds ratios for death were: 2.15 (1.98-2.34) for 1 comorbidity; 4.63 (4.54-4.74) for 2 comorbidities and 4.98 (3.78-6.65) for ≥3 comorbidities. Odds of admission to critical care were increased for all co-morbidities apart from asthma (0.92 (0.91-0.94)) and malignancy (0.85 (0.17-4.21)) with an increased odds of death in all co-morbidities considered apart from asthma. Neurological and cardiac comorbidities were associated with the greatest increase in odds of severe disease or death. Obesity increased the odds of severe disease and death independently of other comorbidities. IPD analysis demonstrated that, compared to children without co-morbidity, the risk difference of admission to critical care was increased in those with 1 comorbidity by 3.61% (1.87-5.36); 2 comorbidities by 9.26% (4.87-13.65); ≥3 comorbidities 10.83% (4.39-17.28), and for death: 1 comorbidity 1.50% (0.00-3.10); 2 comorbidities 4.40% (-0.10-8.80) and ≥3 co-morbidities 4.70 (0.50-8.90).

INTERPRETATION

Hospitalised CYP at greatest vulnerability of severe disease or death with SARS-CoV-2 infection are infants, teenagers, those with cardiac or neurological conditions, or 2 or more comorbid conditions, and those who are obese. These groups should be considered higher priority for vaccination and for protective shielding when appropriate. Whilst odds ratios were high, the absolute increase in risk for most comorbidities was small compared to children without underlying conditions.

FUNDING

RH is in receipt of a fellowship from Kidney Research UK (grant no. TF_010_20171124). JW is in receipt of a Medical Research Council Fellowship (Grant No. MR/R00160X/1). LF is in receipt of funding from Martin House Children's Hospice (there is no specific grant number for this). RV is in receipt of a grant from the National Institute of Health Research to support this work (grant no NIHR202322). Funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript.

Epidemiology of SARS-CoV-2 Infection in Italy Using Real-World Data: Methodology and Cohort Description of the Second Phase of Web-Based EPICOVID19 Study

Digital technologies have been extensively employed in response to the SARS-CoV-2 pandemic worldwide. This study describes the methodology of the two-phase internet-based EPICOVID19 survey, and the characteristics of the adult volunteer respondents who lived in Italy during the first (April-May 2020) and the second wave (January-February 2021) of the epidemic. Validated scales and ad hoc questionnaires were used to collect socio-demographic, medical and behavioural characteristics, as well as information on COVID-19. Among those who provided email addresses during phase I (105,355), 41,473 participated in phase II (mean age 50.7 years ± 13.5 SD, 60.6% females). After a median follow-up of ten months, 52.8% had undergone nasopharyngeal swab (NPS) testing and 13.2% had a positive result. More than 40% had undergone serological test (ST) and 11.9% were positive. Out of the 2073 participants with at least one positive ST, 72.8% had only negative results from NPS or never performed it. These results indicate that a large fraction of individuals remained undiagnosed, possibly contributing to the spread of the virus in the community. Participatory online surveys offer a unique opportunity to collect relevant data at individual level from large samples during confinement.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

Gender Difference in Perceived Symptoms and Laboratory Investigations in Suspected and Confirmed COVID-19 Cases: A Retrospective Study

BACKGROUND

Evaluating gender-specific effects of COVID-19 is important to develop effective therapeutic strategies. The aim of this study was to explore gender difference in perceived symptoms and laboratory investigations in suspected and confirmed cases.

METHODS

This is a retrospective study that included data from suspected COVID-19 patients during the first wave of the pandemic. Participants using the phone triaging system at Kasralainy outpatient clinics were included. The analyzed data included patient history and results of nasopharyngeal swab and laboratory data.

RESULTS

Out of 440 COVID-19 suspected cases, 56.36% were females. The perceived COVID-19 symptoms showed no significant gender difference in suspected cases while in confirmed cases females were 4 times more likely to complain of cough [OR (95% CI) 3.92 (1.316-11.68), P-value .014] and 5 times more likely to experience loss of smell or taste [OR (95% CI) 4.84 (1.62-14.43), P-value .005]. Laboratory markers revealed high levels of aspartate aminotransferase, alanine aminotransferase, blood urea, serum creatinine, creatine kinase, and serum ferritin in males and this was statistically significant (P-value <.001) in suspected and confirmed cases. Females confirmed with COVID-19 were 80%, 97%, and 97% less likely to have high levels of ALT, creatin kinase, and serum ferritin [OR (95% CI) 0.20 (0.07-0.54), 0.07 (0.01-0.38), and 0.07 (0.01-0.90), P-value .002, .002, and .041, respectively].

CONCLUSION

Gender differences were found in laboratory markers in COVID-19 suspected and confirmed cases and in perceived symptoms in confirmed cases.

Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology

Background

Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient’s clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them.

Objective

Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured.

Methods

The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called “EHRead Technology” (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard.

Results

The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs.

Conclusions

We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights

BACKGROUND

With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to "Disease X" (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies "Biodisaster X." To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X.

OBJECTIVE

This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats.

METHODS

A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020.

RESULTS

Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning-based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public's feelings and readiness for recovery building).

CONCLUSIONS

Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.

Sex differences in the case-fatality rates for COVID-19-A comparison of the age-related differences and consistency over seven countries

BACKGROUND

Early in the COVID-19 pandemic, it was noted that males seemed to have higher case-fatality rates than females. We examined the magnitude and consistency of the sex differences in age-specific case-fatality rates (CFRs) in seven countries.

METHODS

Data on the cases and deaths from COVID-19, by sex and age group, were extracted from the national official agencies from Denmark, England, Israel, Italy, Spain, Canada and Mexico. Age-specific CFRs were computed for males and females separately. The ratio of the male to female CFRs were computed and meta-analytic methods were used to obtained pooled estimates of the male to female ratio of the CFRs over the seven countries, for all age-groups. Meta-regression and sensitivity analysis were conducted to evaluate the age and country contribution to differences.

RESULTS

The CFRs were consistently higher in males at all ages. The pooled M:F CFR ratios were 1.71, 1.88, 2.11, 2.11, 1.84, 1.78 and 1.49, for ages 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+ respectively. In meta-regression, age group and country were associated with the heterogeneity in the CFR ratios.

CONCLUSIONS

The sex differences in the age-specific CFRs are intriguing. Sex differences in the incidence and mortality have been found in many infectious diseases. For COVID-19, factors such as sex differences in the prevalence of underlying diseases may play a part in the CFR differences. However, the consistently greater case-fatality rates in males at all ages suggests that sex-related factors impact on the natural history of the disease. This could provide important clues as to the mechanisms underlying the severity of COVID-19 in some patients.