Characteristics and Prognosis of COVID-19 in Patients with COPD

Natural language processing to identify and characterize spondyloarthritis in clinical practice

OBJECTIVE

This study aims to use a novel technology based on natural language processing (NLP) to extract clinical information from electronic health records (EHRs) to characterise the clinical profile of patients diagnosed with spondyloarthritis (SpA) at a large-scale hospital.

METHODS

An observational, retrospective analysis was conducted on EHR data from all patients with SpA (including psoriatic arthritis (PsA)) at Hospital Universitario La Paz, between 2020 and 2022. Data were collected using Savana Manager, an NLP-based system, enabling the extraction of information from unstructured, free-text EHRs. Variables analysed included demographic data, SpA subtypes, comorbidities and treatments. The performance of the technology in detecting SpA clinical entities was evaluated through precision, recall and F-1 score metrics.

RESULTS

From a hospital population of 639 474 patients, 4337 (0.7%) patients had a diagnosis of SpA or their subtypes in their EHR. The population predominantly comprised men (55.3%) with a mean age of 50.9 years. Peripheral SpA (including PsA) was reported in 31.6%, axial SpA in 20.9%, both axial and peripheral SpA in 3.7%, while 43.7% of patients did not have the SpA subtype reported. Common comorbidities included hypertension (25.0%), dyslipidaemia (22.2%) and diabetes mellitus (15.5%). The use of conventional disease-modifying antirheumatic drugs (csDMARDs) and biological DMARDs (bDMARDs) was documented, with methotrexate (25.3% of patients) being the most used csDMARDs and adalimumab (10.6% of patients) the most used bDMARD. The NLP technology demonstrated high precision and recall, with all the assessed F-1 score values over 0.80, indicating reliable data extraction.

CONCLUSION

The application of NLP technology facilitated the characterisation of the SpA patient profile, including demographics, clinical features, comorbidities and treatments. This study supports the utility of NLP in enhancing the understanding of SpA and suggests its potential for improving patient management by extracting meaningful information from unstructured EHR data.

Effectiveness of recently-approved oral antiviral medications on the outcome of patients with mild-to-moderate COVID-19 and pre-existing chronic obstructive pulmonary diseases

OBJECTIVES

This study assessed the effectiveness of the oral antiviral agents nirmatrelvir - ritonavir (NMV-r) and molnupiravir (MOV) for treating mild-to-moderate coronavirus disease 2019 (COVID-19) in patients with COPD.

METHODS

This retrospective cohort study extracted data from the TriNetX platform and examined 94,984 COVID-19 patients with preexisting COPD from 1 January 2022, to 1 October 2023. Patients receiving NMV-r or MOV (study group) were compared with those not receiving oral antiviral agents (control group) after propensity score matching (PSM).

RESULTS

After PSM, 7,944 patients were classified into the study and control groups. The primary composite outcome of all-cause hospitalization, or death in 30 days was reported in 458 (5.7%) patients in the study group and 566 (7.1%) patients in the control cohort, yielding a hazard ratio [HR] of 0.79 (95% confidence interval [CI]: 0.70-0.89; Table 2). Compared with the control group, the study group had a significantly lower risk of all-cause hospitalization (HR, 0.87; 95% CI: 0.76-0.99) and death (HR: 0.21, 95% CI: 0.13-0.35).

CONCLUSIONS

This study revealed that oral antivirals - NMV-r or MOV might improve clinical outcomes in patients with preexisting COPD and COVID-19. However, only a small proportion of preexisting COPD patients with COVID-19 received oral antiviral treatment.

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.

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.

The influence of prior use of inhaled corticosteroids on COVID-19 outcomes: A systematic review and meta-analysis

The influence of inhaled corticosteroids (ICS) on COVID-19 outcomes remains uncertain. To address this, we conducted a systematic review and meta-analysis, analyzing 30 studies, to investigate the impact of ICS on patients with COVID-19. Our study focused on various outcomes, including mortality risk, hospitalization, admission to the intensive care unit (ICU), mechanical ventilation (MV) utilization, and length of hospital stay. Additionally, we conducted a subgroup analysis to assess the effect of ICS on patients with chronic obstructive pulmonary disease (COPD) and asthma. Our findings suggest that the prior use of ICS did not lead to significant differences in mortality risk, ICU admission, hospitalization, or MV utilization between individuals who had used ICS previously and those who had not. However, in the subgroup analysis of patients with COPD, prior ICS use was associated with a lower risk of mortality compared to non-users (OR, 0.95; 95% CI, 0.90-1.00). Overall, while the use of ICS did not significantly affect COVID-19 outcomes in general, it may have beneficial effects specifically for patients with COPD. Nevertheless, more research is needed to establish a definitive conclusion on the role of ICS in COVID-19 treatment. PROSPERO registration number: CRD42021279429.

Application of Artificial intelligence in COVID-19-related geriatric care: A scoping review

BACKGROUND

Older adults have been disproportionately affected by the COVID-19 pandemic. This scoping review aimed to summarize the current evidence of artificial intelligence (AI) use in the screening/monitoring, diagnosis, and/or treatment of COVID-19 among older adults.

METHOD

The review followed the Joanna Briggs Institute and Arksey and O'Malley frameworks. An information specialist performed a comprehensive search from the date of inception until May 2021, in six bibliographic databases. The selected studies considered all populations, and all AI interventions that had been used in COVID-19-related geriatric care. We focused on patient, healthcare provider, and healthcare system-related outcomes. The studies were restricted to peer-reviewed English publications. Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought.

RESULTS

Six databases were searched , yielding 3,228 articles, of which 10 were included. The majority of articles used a single AI model to assess the association between patients' comorbidities and COVID-19 outcomes. Articles were mainly conducted in high-income countries, with limited representation of females in study participants, and insufficient reporting of participants' race and ethnicity.

DISCUSSION

This review highlighted how the COVID-19 pandemic has accelerated the application of AI to protect older populations, with most interventions in the pilot testing stage. Further work is required to measure effectiveness of these technologies in a larger scale, use more representative datasets for training of AI models, and expand AI applications to low-income countries.

Persistent Respiratory Failure and Re-Admission in Patients with Chronic Obstructive Pulmonary Disease Following Hospitalization for COVID-19

Background

Chronic obstructive pulmonary disease (COPD) has been associated with worse clinical evolution/survival during a hospitalization for SARS-CoV2 (COVID-19). The objective of this study was to learn the situation of these patients at discharge as well as the risk of re-admission/mortality in the following 12 months.

Methods

We carried out a subanalysis of the RECOVID registry. A multicenter, observational study that retrospectively collected data on severe acute COVID-19 episodes and follow-up visits for up to a year in survivors. The data collection protocol includes general demographic data, smoking, comorbidities, pharmacological treatment, infection severity, complications during hospitalization and required treatment. At discharge, resting oxygen saturation (SpO2), dyspnea according to the mMRC (modified Medical Research Council) scale and long-term oxygen therapy prescription were recorded. The follow-up database included the clinical management visits at 6 and 12 months, where re-admission and mortality were recorded.

Results

A total of 2047 patients were included (5.6% had a COPD diagnosis). At discharge, patients with COPD had greater dyspnea and a greater need for prescription home oxygen. After adjusting for age, sex and Charlson comorbidity index, patients with COPD had a greater risk of hospital re-admission due to respiratory causes (HR 2.57 [1.35-4.89], p = 0.004), with no significant differences in survival.

Conclusion

Patients with COPD who overcome a serious SARS-CoV2 infection show a worse clinical situation at discharge and a greater risk of re-admission for respiratory causes.

Risk factors identification of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective study in Punjab-Pakistan

BACKGROUND

Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID-19) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID-19 patients. Moreover, by combining the effects of the SARS-CoV-2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers.

MATERIALS AND METHODS

A retrospective study of 280 patients was conducted at DHQ Hospital Muzaffargarh in Punjab, Pakistan. Negative binomial regression describes the risk of fixed successive variables. The association is described by the Cox proportional hazard model and the model coefficient is determined through log-likelihood observation. Patients with COPD had their survival and mortality plotted on Kaplan-Meier curves.

RESULTS

The increased risk of death in COPD patients was due to the effects of variables such as cough, lower respiratory tract infection (LRTI), tuberculosis (TB), and body-aches being 1.369, 0.693, 0.170, and 0.217 times higher at (95% confidence interval [CI]: 0.747-1.992), (95% CI: 0.231-1.156), (95% CI: 0.008-0.332), and (95% CI: -0.07 to 0.440) while it decreased 0.396 in normal condition.

CONCLUSION

We found that the symptoms of COPD (cough, LRTI, TB, and bodyaches) are statistically significant in patients who were most infected by SARS-CoV-2.

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.

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.

Pre- and post-vaccination characteristics and risk factors for COVID-19 outcomes in a Swedish population-based cohort of COPD patients

Rationale

Evidence on risk factors for Coronavirus disease 2019 (COVID-19) outcomes among patients with COPD in relation to COVID-19 vaccination remains limited. The objectives of the present study were to characterise determinants of COVID-19 infection, hospitalisation, intensive care unit (ICU) admission and death in COPD patients in their unvaccinated state compared to when vaccinated.

Methods

We included all COPD patients in the Swedish National Airway Register (SNAR). Events of COVID-19 infection (test and/or healthcare encounter), hospitalisation, ICU admission and death were identified from 1 January 2020 to 30 November 2021. Using adjusted Cox regression, associations between baseline sociodemographics, comorbidities, treatments, clinical measurements and COVID-19 outcomes, during unvaccinated and vaccinated follow-up time, were analysed.

Results

The population-based COPD cohort included 87 472 patients, among whom 6771 (7.7%) COVID-19 infections, 2897 (3.3%) hospitalisations, 233 (0.3%) ICU admissions and 882 (1.0%) COVID-19 deaths occurred. During unvaccinated follow-up, risk of COVID-19 hospitalisation and death increased with age, male sex, lower education, non-married status and being foreign-born. Comorbidities increased risk of several outcomes, e.g. respiratory failure for infection and hospitalisation (adjusted hazard ratios (HR) 1.78, 95% CI 1.58-2.02 and 2.51, 2.16-2.91, respectively), obesity for ICU admission (3.52, 2.29-5.40) and cardiovascular disease for mortality (2.80, 2.16-3.64). Inhaled COPD therapy was associated with infection, hospitalisation and death. COPD severity was also associated with COVID-19, especially hospitalisation and death. Although the risk factor panorama was similar, COVID-19 vaccination attenuated HRs for some risk factors.

Conclusion

This study provides population-based evidence on predictive risk factors for COVID-19 outcomes and highlights the positive implications of COVID-19 vaccination for COPD patients.

Prevalence, attitude, knowledge, and risk perception towards COVID-19 in COPD patients associated to biomass exposure

Chronic obstructive pulmonary disease (COPD) patients due to biomass exposure (BE-COPD) could be more affected than COPD due to tobacco smoke (TE-COPD) by the coronavirus disease 2019 (COVID-19) pandemic. The aim of this work was to determine the prevalence of COVID-19 in BE-COPD and TE-COPD and if housing conditions, poor attitude, knowledge, and risk perception towards COVID-19, particularly in BE-COPD women, could represent a risk factor for contagion.An 11% prevalence of COVID-19 was found with no significant difference between COPD groups. The BE-COPD group showed poorer socioeconomic status. No significant differences were found to be associated with SARS-CoV-2 infection regarding housing conditions, poor knowledge, attitude, and risk perception towards COVID-19. Living in urban areas and perceiving risk in COVID-19 were significantly associated with increased adherence to sanitary measures and concern of contagion. Around 40% of all patients showed poor risk perception and adherence to sanitary measures towards COVID-19.

Efficacy of mRNA and Inactivated Whole Virus Vaccines Against COVID-19 in Patients with Chronic Respiratory Diseases

Background

While different COVID-19 vaccines have been developed, there has been lack of data on the efficacy comparison between mRNA and inactivated whole virus vaccine among patients with chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and bronchiectasis.

Methods

This was a retrospective case control study on the efficacy of BNT162b2 (mRNA vaccine) and CoronaVac (inactivated whole virus vaccine) against COVID-19 in patients with chronic respiratory diseases. A total of 327 patients were included, with 109 patients infected with COVID-19 matched with 218 patients without COVID-19. The co-primary outcomes were vaccine effectiveness against symptomatic COVID-19, COVID-19-related hospitalization and COVID-19-related respiratory failure. Vaccine effectiveness was calculated using the formula (1-adjusted odds ratio) x 100.

Results

Patients who received at least 2 doses of CoronaVac had lower risk of being hospitalized for COVID-19 and developing respiratory failure than those who did not have vaccination, with adjusted odds ratio (OR) of 0.189 (95% CI = 0.050-0.714, p = 0.014) and 0.128 (95% CI = 0.026-0.638, p = 0.012) respectively. Patients who received at least 2 doses of BNT162b2 had lower risk of being hospitalized for COVID-19 and developing respiratory failure than those who did not have vaccination with adjusted OR of 0.207 (95% CI = 0.043-0.962, p = 0.050) and 0.093 (95% CI = 0.011-0.827, p = 0.033) respectively. There was no statistically significant difference in the risks of being hospitalized for COVID-19 and developing respiratory failure between patients who received at least 2 doses of CoronaVac or BNT162b2.

Conclusion

BNT162b2 and CoronaVac vaccines are effective in preventing hospitalization for COVID-19 and respiratory failure complicating COVID-19 among patients with chronic respiratory diseases. Patients with chronic respiratory diseases should be encouraged to have COVID-19 vaccination.

Risk of hospitalization in a sample of COVID-19 patients with and without chronic obstructive pulmonary disease

BACKGROUND AND OBJECTIVE

Patients with chronic obstructive pulmonary disease (COPD) may have worse coronavirus disease-2019 (COVID-19)-related outcomes. We compared COVID-19 hospitalization risk in patients with and without COPD.

METHODS

This retrospective cohort study included patients ≥40 years, SARS-CoV-2 positive, and with Kaiser Permanente Northern California membership ≥1 year before COVID-19 diagnosis (electronic health records and claims data). COVID-19-related hospitalization risk was assessed by sequentially adjusted logistic regression models and stratified by disease severity. Secondary outcome was death/hospice referral after COVID-19.

RESULTS AND DISCUSSION

Of 19,558 COVID-19 patients, 697 (3.6%) had COPD. Compared with patients without COPD, COPD patients were older (median age: 69 vs 53 years); had higher Elixhauser Comorbidity Index (5 vs 0) and more median baseline outpatient (8 vs 4), emergency department (2 vs 1), and inpatient (2 vs 1) encounters. Unadjusted analyses showed increased odds of hospitalization with COPD (odds ratio [OR]: 3.93; 95% confidence interval [CI]: 3.40-4.60). After full risk adjustment, there were no differences in odds of hospitalization (OR: 1.14, 95% CI: 0.93-1.40) or death/hospice referral (OR: 0.96, 95% CI: 0.72-1.27) between patients with and without COPD. Primary/secondary outcomes did not differ by COPD severity, except for higher odds of hospitalization in COPD patients requiring supplemental oxygen versus those without COPD (OR: 1.84, 95% CI: 1.02-3.33).

CONCLUSIONS

Except for hospitalization among patients using supplemental oxygen, no differences in odds of hospitalization or death/hospice referral were observed in the COVID-19 patient sample depending on whether they had COPD.

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.

COVID-19-associated pneumonia in Swyer-James-MacLeod syndrome: A case report

Coronavirus disease 2019 (COVID-19) exerts differential effects on various individuals. The majority of infected individuals experience mild-to-moderate disease and usually recover, without requiring hospitalization. It has been reported that those who have underlying chronic diseases are more susceptible to infection and may thus develop significantly more serious illness. As a result, COVID-19 may aggravate pre-existing respiratory illnesses, such as interstitial lung disease, chronic obstructive pulmonary disease and asthma. Swyer-James-MacLeod syndrome is an uncommon clinical condition marked by post-infectious infantile bronchiolitis obliterans. Traditionally, the diagnosis is made in infancy following an investigation for reoccurring respiratory infections, although in rare cases, the diagnosis is made in adulthood. The present study describes the case of a 45-year-old patient with Swyer-James-MacLeod syndrome hospitalized due to COVID-19, which is the first one to be reported. To the best of our knowledge, there are currently no data available on the effects of COVID-19 in these individuals, stheir optimal therapy, or the impact of COVID-19 vaccination on their clinical course. Thus, it is hoped that the present study sheds some light into this condition.

The association between stroke and COVID-19-related mortality: a systematic review and meta-analysis based on adjusted effect estimates

Objective

To investigate the association between stroke and the risk for mortality among coronavirus disease 2019 (COVID-19) patients.

Methods

We performed systematic searches through electronic databases including PubMed, Embase, Scopus, and Web of Science to identify potential articles reporting adjusted effect estimates on the association of stroke with COVID-19-related mortality. To estimate pooled effects, the random-effects model was applied. Subgroup analyses and meta-regression were performed to explore the possible sources of heterogeneity. The stability of the results was assessed by sensitivity analysis. Publication bias was evaluated by Begg’s test and Egger’s test.

Results

This meta-analysis included 47 studies involving 7,267,055 patients. The stroke was associated with higher COVID-19 mortality (pooled effect = 1.30, 95% confidence interval (CI): 1.16–1.44; I² = 89%, P < 0.01; random-effects model). Subgroup analyses yielded consistent results among area, age, proportion of males, setting, cases, effect type, and proportion of severe COVID-19 cases. Statistical heterogeneity might result from the different effect type according to the meta-regression (P = 0.0105). Sensitivity analysis suggested that our results were stable and robust. Both Begg’s test and Egger’s test indicated that potential publication bias did not exist.

Conclusion

Stroke was independently associated with a significantly increased risk for mortality in COVID-19 patients.

Epidemiology, Healthcare Resource Utilization, and Mortality of Asthma and COPD in COVID-19: A Systematic Literature Review and Meta-Analyses

Purpose

There has been concern that asthma and chronic obstructive pulmonary disease [COPD] increase the risk of developing and exacerbating COVID-19. The effect of medications such as inhaled corticosteroids (ICS) and biologics on COVID-19 is unclear. This systematic literature review analyzed the published evidence on epidemiology and the burden of illness of asthma and COPD, and the use of baseline medicines among COVID-19 populations.

Patients and Methods

Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, Embase®, MEDLINE® and Cochrane were searched (January 2019–August 2021). The prevalence of asthma or COPD among COVID-19 populations was compared to the country-specific populations. Odds ratios (ORs) were estimated to compare healthcare resource utilization (HCRU) rates, and meta-analyses of outcomes were estimated from age-adjusted ORs (aORs) or hazard ratios (aHRs). Meta-analyses of COVID-19 outcomes were conducted using random effects models for binary outcomes.

Results

Given the number and heterogeneity of studies, only 183 high-quality studies were analyzed, which reported hospitalization, intensive care unit (ICU) admissions, ventilation/intubation, or mortality. Asthma patients were not at increased risk for COVID-19–related hospitalization (OR = 1.05, 95% CI: 0.92 to 1.20), ICU admission (OR = 1.21, 95% CI: 0.99 to 1.1.48), ventilation/intubation (OR = 1.24, 95% CI: 0.95 to 1.62), or mortality (OR = 0.85, 95% CI: 0.75 to 0.96). Accounting for confounding variables, COPD patients were at higher risk of hospitalization (aOR = 1.45, 95% CI: 1.30 to 1.61), ICU admission (aOR = 1.28, 95% CI: 1.08 to 1.51), and mortality (aOR = 1.41, 95% CI: 1.37 to 1.65). Sixty-five studies reported outcomes associated with ICS or biologic use. There was limited evidence that ICS or biologics significantly impacted the risk of SARS-CoV-2 infection, HCRU, or mortality in asthma or COPD patients.

Conclusion

In high-quality studies included, patients with asthma were not at significantly higher odds for adverse COVID-19–related outcomes, while patients with COPD were at higher odds. There was no clear evidence that baseline medication affected outcomes.

Registration

PROSPERO (CRD42021233963).

Real-life burden of hospitalizations due to COPD exacerbations in Spain

Patients with chronic obstructive pulmonary disease (COPD) often suffer episodes of exacerbation of symptoms (ECOPD) that may eventually require hospitalization due to several, often overlapping, causes. We aimed to analyse the characteristics of patients hospitalized because of ECOPD in a real-life setting using a big-data approach. The study population included all patients older than 40 years with a diagnosis of COPD (n=69.359; prevalence 3.72%) registered since January 1st, 2011, until March 1, 2020, in the database of the public healthcare service (SESCAM) of Castilla-La Mancha (Spain) (n=1.863.759 subjects). We used natural language processing (Savana Manager v3.0) to identify those who were hospitalized during this period for any cause, including ECOPD. During the study 26.453 COPD patients (38.1%) were hospitalized (at least once). Main diagnoses at discharge were respiratory infection (51%), heart failure (38%) or pneumonia (19%). ECOPD was the main diagnosis at discharge (or hospital death) in 8.331 of them (12.0% of the entire COPD population and 31.5% of those hospitalized). In-hospital ECOPD-related mortality rate was 3.1%. These patients were hospitalized 2.36 times per patient, with a mean hospital stay of 6.1 days. Heart failure (HF) was the most frequent comorbidity in patients hospitalized because of ECOPD (52.6%). This analysis shows that, in a real-life setting, ECOPD hospitalizations are prevalent, complex (particularly in relation to HF), repetitive and associated with significant in-hospital mortality.

Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak

Purpose

COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making. We assessed performances of three machine learning approaches to predict mortality in COVID-19 patients admitted to ICU using early operative data from the Lombardy ICU Network.

Methods

This is a secondary analysis of prospectively collected data from Lombardy ICU network. A logistic regression, balanced logistic regression and random forest were built to predict survival on two datasets: dataset A included patient demographics, medications before admission and comorbidities, and dataset B included respiratory data the first day in ICU.

Results

Models were trained on 1484 patients on four outcomes (7/14/21/28 days) and reached the greatest predictive performance at 28 days (F1-score: 0.75 and AUC: 0.80). Age, number of comorbidities and male gender were strongly associated with mortality. On dataset B, mode of ventilatory assistance at ICU admission and fraction of inspired oxygen were associated with an increase in prediction performances.

Conclusions

Machine learning techniques might be useful in emergency phases to reach good predictive performances maintaining interpretability to gain knowledge on complex situations and enhance patient management and resources.

Clinical course and outcomes of COVID-19 patients with chronic obstructive pulmonary disease: A retrospective observational study in Wuhan, China

Information about coronavirus disease 2019 (COVID-19) patients with pre-existing chronic obstructive pulmonary disease (COPD) is still lacking. The aim of this study is to describe the clinical course and the outcome of COVID-19 patients with comorbid COPD.This retrospective study was performed at Wuhan Huoshenshan Hospital in China. Patients with a clear diagnosis of COVID-19 who had comorbid COPD (N = 78) were identified. COVID-19 patients without COPD were randomly selected and matched by age and sex to those with COPD. Clinical data were analyzed and compared between the two groups. The composite outcome was the onset of intensive care unit admission, use of mechanical ventilation, or death during hospitalization. Multivariable Cox regression analyses controlling for comorbidities were performed to explore the relationship between comorbid COPD and clinical outcome of COVID-19.Compared to age- and sex-matched COVID-19 patients without pre-existing COPD, patients with pre-existing COPD were more likely to present with dyspnea, necessitate expectorants, sedatives, and mechanical ventilation, suggesting the existence of acute exacerbations of COPD (AECOPD). Greater proportions of patients with COPD developed respiratory failure and yielded poor clinical outcomes. However, laboratory tests did not show severer infection, over-activated inflammatory responses, and multi-organ injury in patients with COPD. Kaplan-Meier analyses showed patients with COPD exhibited longer viral clearance time in the respiratory tract. Multifactor regression analysis showed COPD was independently correlated with poor clinical outcomes.COVID-19 patients with pre-existing COPD are more vulnerable to AECOPD and subsequent respiratory failure, which is the main culprit for unfavorable clinical outcomes. However, COPD pathophysiology itself is not associated with over-activated inflammation status seen in severe COVID-19.

D-dimer Levels in Predicting Severity of Infection and Outcome in Patients with COVID-19

COVID-19 disease began to spread all around the world in December 2019 until now; and in the early stage it may be related to high D-dimer level that indicates coagulation pathways and thrombosis activation that can be affected by some underlying diseases including diabetes, stroke, cancer, and pregnancy and it also can be associated with Chronic obstructive pulmonary disease (COPD). The aim of this article was to analyze D-dimer levels in COVID-19 patients, as D-dimer level is one of the measures to detect the severity and outcomes of COVID-19. According to the results of this study, there is a higher level of D-dimer as well as concentrations of fibrinogen in the disease onset and it seems that the poor prognosis is linked to a 3 to 4-fold increase in D-dimer levels. It is also shown that 76% of the patients with ≥1 D-dimer measurement, had elevated D-dimer and were more likely to have critical illness than those with normal D-dimer. There was an increase in the rates of adverse outcomes with higher D-dimer of more than 2000 ng/mL and it is associated with the highest risk of death at 47%, thrombotic event at 37.8%, and critical illness at 66%. It also found that diabetes and COPD had the strongest association with death in COVID-19. So, it is necessary to measure the D-dimer levels and parameters of coagulation from the beginning as well as pay attention to comorbidities that can help control and management of COVID-19 disease.

Chronic Obstructive Pulmonary Disease Combined with Interstitial Lung Disease

Although chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) have distinct clinical features, both diseases may coexist in a patient because they share similar risk factors such as smoking, male sex, and old age. Patients with both emphysema in upper lung fields and diffuse ILD are diagnosed with combined pulmonary fibrosis and emphysema (CPFE), which causes substantial clinical deterioration. Patients with CPFE have higher mortality compared with patients who have COPD alone, but results have been inconclusive compared with patients who have idiopathic pulmonary fibrosis (IPF). Poor prognostic factors for CPFE include exacerbation, lung cancer, and pulmonary hypertension. The presence of interstitial lung abnormalities, which may be an early or mild form of ILD, is notable among patients with COPD, and is associated with poor prognosis. Various theories have been proposed regarding the pathophysiology of CPFE. Biomarker analyses have implied that this pathophysiology may be more closely associated with IPF development, rather than COPD or emphysema. Patients with CPFE should be advised to quit smoking and undergo routine lung function tests, and pulmonary rehabilitation may be helpful. Various pharmacologic agents and surgical approaches may be beneficial in patients with CPFE, but further studies are needed.

Short and Long-Term Impact of COVID-19 Infection on Previous Respiratory Diseases

On March 11, 2020, the World Health Organization declared Coronavirus Disease 2019 (COVID-19) a pandemic. Till now, it affected 452.4 million (Spain, 11.18 million) persons all over the world with a total of 6.04 million of deaths (Spain, 100,992). It is observed that 75% of hospitalized COVID-19 patients have at least one COVID-19 associated comorbidity. It was shown that people with underlying chronic illnesses are more likely to get it and grow seriously ill. Individuals with COVID-19 who have a past medical history of cardiovascular disorder, cancer, obesity, chronic lung disease, diabetes, or neurological disease had the worst prognosis and are more likely to develop acute respiratory distress syndrome or pneumonia. COVID-19 can affect the respiratory system in a variety of ways and across a spectrum of levels of disease severity, depending on a person's immune system, age and comorbidities. Symptoms can range from mild, such as cough, shortness of breath and fever, to critical disease, including respiratory failure, shock and multi-organ system failure. So, COVID-19 infection can cause overall worsening of these previous respiratory diseases, such as asthma, chronic obstructive pulmonary disease (COPD), interstitial lung disease, etc. This review aims to provide information on the impact of the COVID-19 disease on pre-existing lung comorbidities.

Clinical characteristics and prognostic factors for Crohn's disease relapses using natural language processing and machine learning: a pilot study

BACKGROUND

The impact of relapses on disease burden in Crohn's disease (CD) warrants searching for predictive factors to anticipate relapses. This requires analysis of large datasets, including elusive free-text annotations from electronic health records. This study aims to describe clinical characteristics and treatment with biologics of CD patients and generate a data-driven predictive model for relapse using natural language processing (NLP) and machine learning (ML).

METHODS

We performed a multicenter, retrospective study using a previously validated corpus of CD patient data from eight hospitals of the Spanish National Healthcare Network from 1 January 2014 to 31 December 2018 using NLP. Predictive models were created with ML algorithms, namely, logistic regression, decision trees, and random forests.

RESULTS

CD phenotype, analyzed in 5938 CD patients, was predominantly inflammatory, and tobacco smoking appeared as a risk factor, confirming previous clinical studies. We also documented treatments, treatment switches, and time to discontinuation in biologics-treated CD patients. We found correlations between CD and patient family history of gastrointestinal neoplasms. Our predictive model ranked 25 000 variables for their potential as risk factors for CD relapse. Of highest relative importance were past relapses and patients' age, as well as leukocyte, hemoglobin, and fibrinogen levels.

CONCLUSION

Through NLP, we identified variables such as smoking as a risk factor and described treatment patterns with biologics in CD patients. CD relapse prediction highlighted the importance of patients' age and some biochemistry values, though it proved highly challenging and merits the assessment of risk factors for relapse in a clinical setting.

Mortality, seasonal variation, and susceptibility to acute exacerbation of COPD in the pandemic year: a nationwide population study

Background:

Previous studies have suggested that the coronavirus disease 2019 (COVID-19) pandemic was associated with a decreased rate of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Data on how the COVID-19 pandemic has influenced mortality, seasonality of, and susceptibility to AECOPD in the chronic obstructive pulmonary disease (COPD) population is scarce.

Methods:

We conducted a national population-based retrospective study using data from the Health Insurance Institute of Slovenia from 2015 to February 2021, with 2015–2019 as the reference. We extracted patient and healthcare data for AECOPD, dividing AECOPD into severe, resulting in hospitalisation, and moderate, requiring outpatient care. The national COPD population was generated based on dispensed prescriptions of inhalation therapies, and moderate AECOPD events were analysed based on dispensed AECOPD medications. We extracted data on all-cause and non-COVID mortality.

Results:

The numbers of severe and moderate AECOPD were reduced by 48% and 34%, respectively, in 2020. In the pandemic year, the seasonality of AECOPD was reversed, with a 1.5-fold higher number of severe AECOPD in summer compared to winter. The proportion of frequent exacerbators (⩾2 AECOPD hospitalisations per year) was reduced by 9% in 2020, with a 30% reduction in repeated severe AECOPD in frequent exacerbators and a 34% reduction in persistent frequent exacerbators (⩾2 AECOPD hospitalisations per year for 2 consecutive years) from 2019. The risk of two or more moderate AECOPD decreased by 43% in 2020. In the multivariate model, pandemic year follow-up was the only independent factor associated with a decreased risk for severe AECOPD (hazard ratio [HR]: 0.71; 95% confidence interval [CI]: 0.61–0.84; p < 0.0001). In 2020, non-COVID mortality decreased (−15%) and no excessive mortality was observed in the COPD population.

Conclusion:

In the pandemic year, we found decreased susceptibility to AECOPD across severity spectrum of COPD, reversed seasonal distribution of severe AECOPD and decreased non-COVID mortality in the COPD population.

Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project

Background

The exploration of clinically relevant information in the free text of electronic health records (EHRs) holds the potential to positively impact clinical practice as well as knowledge regarding Crohn disease (CD), an inflammatory bowel disease that may affect any segment of the gastrointestinal tract. The EHRead technology, a clinical natural language processing (cNLP) system, was designed to detect and extract clinical information from narratives in the clinical notes contained in EHRs.

Objective

The aim of this study is to validate the performance of the EHRead technology in identifying information of patients with CD.

Methods

We used the EHRead technology to explore and extract CD-related clinical information from EHRs. To validate this tool, we compared the output of the EHRead technology with a manually curated gold standard to assess the quality of our cNLP system in detecting records containing any reference to CD and its related variables.

Results

The validation metrics for the main variable (CD) were a precision of 0.88, a recall of 0.98, and an F1 score of 0.93. Regarding the secondary variables, we obtained a precision of 0.91, a recall of 0.71, and an F1 score of 0.80 for CD flare, while for the variable vedolizumab (treatment), a precision, recall, and F1 score of 0.86, 0.94, and 0.90 were obtained, respectively.

Conclusions

This evaluation demonstrates the ability of the EHRead technology to identify patients with CD and their related variables from the free text of EHRs. To the best of our knowledge, this study is the first to use a cNLP system for the identification of CD in EHRs written in Spanish.

Immune Response Is Key to Genetic Mechanisms of SARS-CoV-2 Infection With Psychiatric Disorders Based on Differential Gene Expression Pattern Analysis

Existing evidence demonstrates that coronavirus disease 2019 (COVID-19) leads to psychiatric illness, despite its main clinical manifestations affecting the respiratory system. People with mental disorders are more susceptible to COVID-19 than individuals without coexisting mental health disorders, with significantly higher rates of severe illness and mortality in this population. The incidence of new psychiatric diagnoses after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also remarkably high. SARS-CoV-2 has been reported to use angiotensin-converting enzyme-2 (ACE2) as a receptor for infecting susceptible cells and is expressed in various tissues, including brain tissue. Thus, there is an urgent need to investigate the mechanism linking psychiatric disorders to COVID-19. Using a data set of peripheral blood cells from patients with COVID-19, we compared this to data sets of whole blood collected from patients with psychiatric disorders and used bioinformatics and systems biology approaches to identify genetic links. We found a large number of overlapping immune-related genes between patients infected with SARS-CoV-2 and differentially expressed genes of bipolar disorder (BD), schizophrenia (SZ), and late-onset major depressive disorder (LOD). Many pathways closely related to inflammatory responses, such as MAPK, PPAR, and TGF-β signaling pathways, were observed by enrichment analysis of common differentially expressed genes (DEGs). We also performed a comprehensive analysis of protein-protein interaction network and gene regulation networks. Chemical-protein interaction networks and drug prediction were used to screen potential pharmacologic therapies. We hope that by elucidating the relationship between the pathogenetic processes and genetic mechanisms of infection with SARS-CoV-2 with psychiatric disorders, it will lead to innovative strategies for future research and treatment of psychiatric disorders linked to COVID-19.

Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients

The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios.

[Handling of COVID-19 Outbreak on a Non-invasive Ventilation Ward]

COPD patients have a higher risk of experiencing severe COVID-19 illness. The outbreak of COVID-19 on an in-patient ward for non-invasive ventilation (NIV) furthermore demonstrated high mortality (32 %) for COPD patients with ongoing NIV and indicated enhanced contagiousness by used equipment.Prophylactic and therapeutic measures taken against COVID-19 are hereby displayed.

Diabetes, Heart Failure, and COVID-19: An Update

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a pandemic by the WHO in March 2020. As of August 2021, more than 220 countries have been affected, accounting for 211,844,613 confirmed cases and 4,432,802 deaths worldwide. A new delta variant wave is sweeping through the globe. While previous reports consistently have demonstrated worse prognoses for patients with existing cardiovascular disease than for those without, new studies are showing a possible link between SARS-CoV-2 infection and an increased incidence of new-onset heart disease and diabetes, regardless of disease severity. If this trend is true, with hundreds of millions infected, the disease burden could portend a potentially troubling increase in heart disease and diabetes in the future. Focusing on heart failure in this review, we discuss the current data at the intersection of COVID, heart failure, and diabetes, from clinical findings to potential mechanisms of how SARS-CoV-2 infection could increase the incidence of those pathologies. Additionally, we posit questions for future research areas regarding the significance for patient care.

Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

Objectives: To analyze the content of publications within the medical NLP domain in 2020.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.

Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

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.

Impact of the COVID-19 pandemic on the management of chronic noninfectious respiratory diseases

Introduction: The COVID-19 pandemic has challenged health care across the world, not just by the severity of the disease and the high mortality rate but also by the consequences on the management of the patients with chronic diseases.Areas covered: This review summarizes the most up-to-date published data regarding the impact of COVID-19 on the management and outcomes of patients with chronic noninfectious respiratory illnesses including obstructive sleep apnea, asthma, chronic obstructive pulmonary disease, bronchiectasis, interstitial and pulmonary vascular diseases, and lung cancer.Expert opinion: Most of chronic respiratory diseases (except asthma and cystic fibrosis) are associated with more severe COVID-19 and poor outcomes but the mechanisms involved are not yet identified. The therapeutic management of the patients with chronic respiratory diseases and COVID-19 is similar to the other patients but the post-recovery course could be worse in this population and followed by the development of pulmonary fibrosis, bronchiectasis, and pulmonary hypertension. The pandemic highly impacted our usual medical activities by limiting the access to several diagnosis procedures, the necessity to develop new methods for the monitoring of the disease and adapt the therapeutic strategies. The long-term consequences of all these changes are still unknown.

Covid-19 and non-communicable diseases: evidence from a systematic literature review

BACKGROUND

Since early 2020, the Covid-19 pandemic has engulfed the world. Amidst the growing number of infections and deaths, there has been an emphasis of patients with non-communicable diseases as they are particularly susceptible to the virus. The objective of this literature review is to systematize the available evidence on the link between non-communicable diseases and Covid-19.

METHODS

We have conducted a systematic review of the literature on Covid-19 and non-communicable diseases from December, 2019 until 15th of November, 2020. The search was done in PubMed and in doing so we used a variety of searching terms in order to isolate the final set of papers. At the end of the selection process, 45 papers were selected for inclusion in the literature review.

RESULTS

The results from the review indicate that patients with certain chronic illnesses such as diabetes, hypertension (and other cardiovascular diseases), chronic respiratory illnesses, chronic kidney and liver conditions are more likely to be affected by Covid-19. More importantly, once they do get infected by the virus, patients with chronic illnesses have a much higher likelihood of having worse clinical outcomes (developing a more severe form of the disease or dying) than an average patient. There are two hypothesized channels that explain this strong link between the chronic illnesses enumerated above and Covid 19: (i) increased ACE2 (angiotensin-converting enzyme 2) receptor expressions, which facilitates the entry of the virus into the host body; and (ii) hyperinflammatory response, referred to as "cytokine storm". Finally, the literature review does not find any evidence that diabetes or hypertension related medications exacerbate the overall Covid-19 condition in chronic illness patients.

CONCLUSIONS

Thus, the evidence points out to 'business as usual' disease management model, although with greater supervision. However, given the ongoing Covid-19 vulnerabilities among people with NCDs, prioritizing them for the vaccination process should also figure high on the agenda on health authorities.

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient's survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning-based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

Clinical characteristics and outcomes of hospitalized COVID-19 patients with COPD

Background

Although COPD is not one of the most common comorbidities in COVID-19 patients, it can be more fatal in this group. This study aimed to investigate the characteristics and prognosis of COPD patients among the population with COVID-19.

Research design and methods

Patients diagnosed with positive PCR test were included in our multicentered, retrospective study. Patients with airway obstruction (previous spirometry) were included in ‘COPD group’.

Results

The prevalence of COPD in COVID-19 patients was 4.96%(53/1069). There was a significant difference between COPD and non-COPD COVID-19 patients in terms of gender, mean age, presence of dyspnea, tachypnea, tachycardia, hypoxemia and presence of pneumonia. The mortality rate was 13.2% in COPD, 7% in non-COPD patients(p = 0.092). The significant predictors of mortality were higher age, lymphopenia (p < 0.001), hypoxemia (p = 0.028), high D-dimer level (p = 0.011), and presence of pneumonia (p = 0.043) in COVID-19 patients.

Conclusions

Our research is one of the first studies investigating characteristics of COPD patients with COVID-19 in Turkey. Although COPD patients had some poor prognostic features, there was no statistical difference between overall survival rates of two groups. Age, status of oxygenization, serum D-dimer level, lymphocyte count and pneumonia were significantly associated parameters with mortality in COVID-19.

How to Manage COVID-19 Vaccination in Immune-Mediated Inflammatory Diseases: An Expert Opinion by IMIDs Study Group

Since March 2020, the outbreak of Sars-CoV-2 pandemic has changed medical practice and daily routine around the world. Huge efforts from pharmacological industries have led to the development of COVID-19 vaccines. In particular two mRNA vaccines, namely the BNT162b2 (Pfizer-BioNTech) and the mRNA-1273 (Moderna), and a viral-vectored vaccine, i.e. ChAdOx1 nCoV-19 (AstraZeneca), have recently been approved in Europe. Clinical trials on these vaccines have been published on the general population showing a high efficacy with minor adverse events. However, specific data about the efficacy and safety of these vaccines in patients with immune-mediated inflammatory diseases (IMIDs) are still lacking. Moreover, the limited availability of these vaccines requires prioritizing some vulnerable categories of patients compared to others. In this position paper, we propose the point of view about the management of COVID-19 vaccination from Italian experts on IMIDs and the identification of high-risk groups according to the different diseases and their chronic therapy.