Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

MOTIVATION

The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA).

RESULTS

Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA's higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert.

AVAILABILITY AND IMPLEMENTATION

Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessing the Burden and Cost of COVID-19 Across Variants in Commercially Insured Immunocompromised Populations in the United States: Updated Results and Trends from the Ongoing EPOCH-US Study

INTRODUCTION/METHODS

EPOCH-US is an ongoing, retrospective, observational cohort study among individuals identified in the Healthcare Integrated Research Database (HIRD®) with ≥ 12 months of continuous health plan enrollment. Data were collected for the HIRD population (containing immunocompetent and immunocompromised [IC] individuals), individual IC cohorts (non-mutually exclusive cohorts based on immunocompromising condition and/or immunosuppressive [IS] treatment), and the composite IC population (all unique IC individuals). This study updates previous results with addition of the general population cohort and data specifically for the year of 2022 (i.e., Omicron wave period). To provide healthcare decision-makers the most recent trends, this study reports incidence rates (IR) and severity of first SARS-CoV-2 infection; and relative risk, healthcare utilization, and costs related to first COVID-19 hospitalizations in the full year of 2022 and overall between April 2020 and December 2022.

RESULTS

These updated results showed a 2.9% prevalence of immune compromise in the population. From April 2020 through December 2022, the overall IR of COVID-19 was 115.7 per 1000 patient-years in the composite IC cohort and 77.8 per 1000 patient-years in the HIRD cohort. The composite IC cohort had a 15.4% hospitalization rate with an average cost of $42,719 for first COVID-19 hospitalization. Comparatively, the HIRD cohort had a 3.7% hospitalization rate with an average cost of $28,848 for first COVID-19 hospitalization. Compared to the general population, IC individuals had 4.3 to 23 times greater risk of hospitalization with first diagnosis of COVID-19. Between January and December 2022, hospitalizations associated with first COVID-19 diagnosis cost over $1 billion, with IC individuals (~ 3% of the population) generating $310 million (31%) of these costs.

CONCLUSION

While only 2.9% of the population, IC individuals had a higher risk of COVID-19 hospitalization and incurred higher healthcare costs across variants. They also disproportionately accounted for over 30% of total costs for first COVID-19 hospitalization in 2022, amounting to ~ $310 million. These data highlight the need for additional preventive measures to decrease the risk of developing severe COVID-19 outcomes in vulnerable IC populations.

Comparative analysis of symptom profile and risk of death associated with infection by SARS-CoV-2 and its variants in Hong Kong

The recurrent multiwave nature of coronavirus disease 2019 (COVID-19) necessitates updating its symptomatology. We characterize the effect of variants on symptom presentation, identify the symptoms predictive and protective of death, and quantify the effect of vaccination on symptom development. With the COVID-19 cases reported up to August 25, 2022 in Hong Kong, an iterative multitier text-matching algorithm was developed to identify symptoms from free text. Multivariate regression was used to measure associations between variants, symptom development, death, and vaccination status. A least absolute shrinkage and selection operator technique was used to identify a parsimonious set of symptoms jointly associated with death. Overall, 70.9% (54 450/76 762) of cases were symptomatic with 102 symptoms identified. Intrinsically, the wild-type and delta variant caused similar symptoms among unvaccinated symptomatic cases, whereas the wild-type and omicron BA.2 subvariant had heterogeneous patterns, with seven symptoms (fatigue, fever, chest pain, runny nose, sputum production, nausea/vomiting, and sore throat) more frequent in the BA.2 cohort. With ≥2 vaccine doses, BA.2 was more likely than delta to cause fever among symptomatic cases. Fever, blocked nose, pneumonia, and shortness of breath remained jointly predictive of death among unvaccinated symptomatic elderly in the wild-type-to-omicron transition. Number of vaccine doses required for reducing occurrence varied by symptoms. We substantiate that omicron has a different clinical presentation compared to previous variants. Syndromic surveillance can be bettered with reduced reliance on symptom-based case identification, increased weighing on symptoms predictive of death in outcome prediction, individual-based risk assessment in care homes, and incorporating free-text symptom reporting.

Assessing the risk and costs of COVID-19 in immunocompromised populations in a large United States commercial insurance health plan: the EPOCH-US Study

OBJECTIVE

To estimate the prevalence of patients with an immunocompromising condition at risk for COVID-19, estimate COVID-19 prevalence rate (PR) and incidence rate (IR) by immunocompromising condition, and describe COVID-19-related healthcare resource utilization (HCRU) and costs.

METHODS

Using the Healthcare Integrated Research Database (HIRD), patients with ≥1 claim for an immunocompromising condition of interest or ≥2 claims for an immunosuppressive (IS) treatment and COVID-19 diagnosis during the infection period (1 April 2020-31 March 2022) and had ≥12 months baseline data were included. Cohorts (other than the composite cohort) were not mutually exclusive and were defined by each immunocompromising condition. Analyses were descriptive in nature.

RESULTS

Of the 16,873,161 patients in the source population, 2.7% (n = 458,049) were immunocompromised (IC). The COVID-19 IR for the composite IC cohort during the study period was 101.3 per 1000 person-years and the PR was 13.5%. The highest IR (195.0 per 1000 person-years) and PR (20.1%) were seen in the end-stage renal disease (ESRD) cohort; the lowest IR (68.3 per 1000 person-years) and PR (9.4%) were seen in the hematologic or solid tumor malignancy cohort. Mean costs for hospitalizations associated with the first COVID-19 diagnosis were estimated at nearly $1 billion (2021 United States dollars [USD]) for 14,516 IC patients, with a mean cost of $64,029 per patient.

CONCLUSIONS

Immunocompromised populations appear to be at substantial risk of severe COVID-19 outcomes, leading to increased costs and HCRU. Effective prophylactic options are still needed for these high-risk populations as the COVID-19 landscape evolves.

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida

Introduction

The SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the "mortality" of patients hospitalized with COVID-19.

Methods

We conducted a retrospective analysis of "5,371" patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients' sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict "mortality" for patients hospitalized with COVID-19.

Results

Based on the interpretability of the model, age emerged as the primary predictor of "mortality", followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as "older adults"), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted "mortality". These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict "mortality" with transparency and reliability.

Conclusion

AI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ).

COVID-19 pandemic: A multidisciplinary perspective on the pathogenesis of a novel coronavirus from infection, immunity and pathological responses

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2), has spread to more than 200 countries and regions, having a huge impact on human health, hygiene, and economic activities. The epidemiological and clinical phenotypes of COVID-19 have increased since the onset of the epidemic era, and studies into its pathogenic mechanisms have played an essential role in clinical treatment, drug development, and prognosis prevention. This paper reviews the research progress on the pathogenesis of the novel coronavirus (SARS-CoV-2), focusing on the pathogenic characteristics, loci of action, and pathogenic mechanisms leading to immune response malfunction of SARS-CoV-2, as well as summarizing the pathological damage and pathological manifestations it causes. This will update researchers on the latest SARS-CoV-2 research and provide directions for future therapeutic drug development.

The risk profile of patients with COVID-19 as predictors of lung lesions severity and mortality—Development and validation of a prediction model

Objective

We developed and validated a prediction model based on individuals' risk profiles to predict the severity of lung involvement and death in patients hospitalized with coronavirus disease 2019 (COVID-19) infection.

Methods

In this retrospective study, we studied hospitalized COVID-19 patients with data on chest CT scans performed during hospital stay (February 2020-April 2021) in a training dataset (TD) (n = 2,251) and an external validation dataset (eVD) (n = 993). We used the most relevant demographical, clinical, and laboratory variables (n = 25) as potential predictors of COVID-19-related outcomes. The primary and secondary endpoints were the severity of lung involvement quantified as mild (≤25%), moderate (26–50%), severe (>50%), and in-hospital death, respectively. We applied random forest (RF) classifier, a machine learning technique, and multivariable logistic regression analysis to study our objectives.

Results

In the TD and the eVD, respectively, the mean [standard deviation (SD)] age was 57.9 (18.0) and 52.4 (17.6) years; patients with severe lung involvement [n (%):185 (8.2) and 116 (11.7)] were significantly older [mean (SD) age: 64.2 (16.9), and 56.2 (18.9)] than the other two groups (mild and moderate). The mortality rate was higher in patients with severe (64.9 and 38.8%) compared to moderate (5.5 and 12.4%) and mild (2.3 and 7.1%) lung involvement. The RF analysis showed age, C reactive protein (CRP) levels, and duration of hospitalizations as the three most important predictors of lung involvement severity at the time of the first CT examination. Multivariable logistic regression analysis showed a significant strong association between the extent of the severity of lung involvement (continuous variable) and death; adjusted odds ratio (OR): 9.3; 95% CI: 7.1–12.1 in the TD and 2.6 (1.8–3.5) in the eVD.

Conclusion

In hospitalized patients with COVID-19, the severity of lung involvement is a strong predictor of death. Age, CRP levels, and duration of hospitalizations are the most important predictors of severe lung involvement. A simple prediction model based on available clinical and imaging data provides a validated tool that predicts the severity of lung involvement and death probability among hospitalized patients with COVID-19.

Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units

Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)-hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys-asthma, cough and fever; class 3, mean age 74.5 ys-hypertension, diabetes and cough; class 4, mean age 67.8 ys-hypertension and no symptoms; class 5, mean age 53 ys-cough and no comorbidities; class 6, mean age 60 ys-without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson's disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality.

The MedConnect Program: Symptomatology, Return Visits, and Hospitalization of COVID-19 Outpatients Following Discharge From the Emergency Department

Background and objective

Although hospitalization is required for only a minority of those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the high rates of morbidity and mortality among these patients have led researchers to focus on the predictors of admission and adverse outcomes in the inpatient population. However, there is scarce data on the clinical trajectory of individuals symptomatic enough to present for emergency care, but not sick enough to be admitted. In light of this, we aimed to examine the symptomatology, emergency department (ED) revisits, and hospitalization of coronavirus disease 2019 (COVID-19) outpatients after discharge from the ED.

Methods

Adult patients with COVID-19 infection were prospectively enrolled after discharge from the ED between May and December 2020. Patients were followed up longitudinally for 14 days via phone interviews designed to provide support and information and to track symptomatology, ED revisits, and hospitalization.

Results

A volunteer, medical student-run program enrolled 199 COVID-19 patients discharged from the ED during the first nine months of the pandemic. Of the 176 patients (88.4%) who completed the 14-day protocol, 29 (16.5%) had a second ED visit and 17 (9.6%) were admitted, 16 (9%) for worsening COVID-19 symptoms. Age, male sex, comorbid illnesses, and self-reported dyspnea, diarrhea, chills, and fever were associated with hospital admission for patients with a subsequent ED visit. For those who did not require admission, symptoms generally improved following ED discharge. Age >65 years and a history of cardiovascular disease (CVD) were associated with a longer duration of cough, but generally, patient characteristics and comorbidities did not significantly affect the overall number or duration of symptoms.

Conclusions

Nearly one in five patients discharged from the ED with COVID-19 infection had a second ED evaluation during a 14-day follow-up period, despite regular phone interactions aimed at providing support and information. More than half of them required admission for worsening COVID-19 symptoms. Established risk factors for severe disease and self-reported persistence of certain symptoms were associated with hospital admission, while those who did not require hospitalization had a steady improvement in symptoms over the 14-day period.

Oxidative Stress-Related Mechanisms in SARS-CoV-2 Infections

The COVID-19 pandemic caused relatively high mortality in patients, especially in those with concomitant diseases (i.e., diabetes, hypertension, and chronic obstructive pulmonary disease (COPD)). In most of aforementioned comorbidities, the oxidative stress appears to be an important player in their pathogenesis. The direct cause of death in critically ill patients with COVID-19 is still far from being elucidated. Although some preliminary data suggests that the lung vasculature injury and the loss of the functioning part of pulmonary alveolar population are crucial, the precise mechanism is still unclear. On the other hand, at least two classes of medications used with some clinical benefits in COVID-19 treatment seem to have a major influence on ROS (reactive oxygen species) and RNS (reactive nitrogen species) production. However, oxidative stress is one of the important mechanisms in the antiviral immune response and innate immunity. Therefore, it would be of interest to summarize the data regarding the oxidative stress in severe COVID-19. In this review, we discuss the role of oxidative and antioxidant mechanisms in severe COVID-19 based on available studies. We also present the role of ROS and RNS in other viral infections in humans and in animal models. Although reactive oxygen and nitrogen species play an important role in the innate antiviral immune response, in some situations, they might have a deleterious effect, e.g., in some coronaviral infections. The understanding of the redox mechanisms in severe COVID-19 disease may have an impact on its treatment.

Characteristics and clinical outcomes of patients hospitalized with laboratory-confirmed COVID-19-Puerto Rico, March-August 2020

Hispanics are the majority ethnic population in Puerto Rico where we reviewed charts of 109 hospitalized COVID-19 patients to better understand demographic and clinical characteristics of COVID-19 and determine risk factors for poor outcomes. Eligible medical records of hospitalized patients with confirmed COVID-19 illnesses were reviewed at four participating hospitals in population centers across Puerto Rico and data were abstracted that described the clinical course, interventions, and outcomes. We found hospitalized patients had a median of 3 underlying conditions with obesity and diabetes as the most frequently reported conditions. Intensive care unit (ICU) admission occurred among 28% of patients and 18% of patients died during the hospitalization. Patients 65 or older or with immune deficiencies had a higher risk for death. Common symptoms included cough, dyspnea, and fatigue; less than half of patients in the study reported fever which was less frequent than reported elsewhere in the literature. It is important for interventions within Hispanic communities to protect high-risk groups.

Association between initial symptoms and subsequent hospitalization in outpatients with COVID-19: A cohort study

BACKGROUND

Most individuals with coronavirus disease 2019 (COVID-19) experience mild symptoms and are managed in the outpatient setting. The aim of this study was to determine whether self-reported symptoms at the time of diagnosis can identify patients at risk of clinical deterioration.

METHODS

This was a retrospective cohort study of 671 outpatients with laboratory-confirmed COVID-19 diagnosed in Toronto between March 1 and October 16, 2020. We examined the association between patients' baseline characteristics and self-reported symptoms at the time of diagnosis and the risk of subsequent hospitalization.

RESULTS

Of 671 participants, 26 (3.9%) required hospitalization. Individuals aged 65 years or older were more likely to require hospitalization (odds ratio [OR] 5.29, 95% CI 2.19 to 12.77), whereas those without medical comorbidities were unlikely to be hospitalized (OR 0.02, 95% CI 0.00 to 0.17). After adjusting for age and presence of comorbidities, sputum production (adjusted OR [aOR] 5.01, 95% CI 1.97 to 12.75), arthralgias (aOR 4.82, 95% CI 1.85 to 12.53), diarrhea (aOR 4.56, 95% CI 1.82 to 11.42), fever (aOR 3.64, 95% CI 1.50 to 8.82), chills (aOR 3.62, 95% CI 1.54 to 8.50), and fatigue (aOR 2.59, 95% CI 1.04 to 6.47) were associated with subsequent hospitalization.

CONCLUSIONS

Early assessment of symptoms among outpatients with COVID-19 can help identify individuals at risk of clinical deterioration. Additional studies are needed to determine whether more intense follow-up and early intervention among high-risk individuals can alter the clinical trajectory of and outcomes among outpatients with COVID-19.

Myasthenic Crisis After Recurrent COVID-19 Infection

A patient with myasthenia gravis who survived 2 COVID-19 infections required plasmapheresis to recover from an acute crisis.

A meta-analysis on the risk factors adjusted association between cardiovascular disease and COVID-19 severity

BACKGROUND

Cardiovascular disease (CVD), one of the most common comorbidities of coronavirus disease 2019 (COVID-19), has been suspected to be associated with adverse outcomes in COVID-19 patients, but their correlation remains controversial.

METHOD

This is a quantitative meta-analysis on the basis of adjusted effect estimates. PubMed, Web of Science, MedRxiv, Scopus, Elsevier ScienceDirect, Cochrane Library and EMBASE were searched comprehensively to obtain a complete data source up to January 7, 2021. Pooled effects (hazard ratio (HR), odds ratio (OR)) and the 95% confidence intervals (CIs) were estimated to evaluate the risk of the adverse outcomes in COVID-19 patients with CVD. Heterogeneity was assessed by Cochran's Q-statistic, I2test, and meta-regression. In addition, we also provided the prediction interval, which was helpful for assessing whether the variation across studies was clinically significant. The robustness of the results was evaluated by sensitivity analysis. Publication bias was assessed by Begg's test, Egger's test, and trim-and-fill method.

RESULT

Our results revealed that COVID-19 patients with pre-existing CVD tended more to adverse outcomes on the basis of 203 eligible studies with 24,032,712 cases (pooled ORs = 1.41, 95% CIs: 1.32-1.51, prediction interval: 0.84-2.39; pooled HRs = 1.34, 95% CIs: 1.23-1.46, prediction interval: 0.82-2.21). Further subgroup analyses stratified by age, the proportion of males, study design, disease types, sample size, region and disease outcomes also showed that pre-existing CVD was significantly associated with adverse outcomes among COVID-19 patients.

CONCLUSION

Our findings demonstrated that pre-existing CVD was an independent risk factor associated with adverse outcomes among COVID-19 patients.

Predicting respiratory failure for COVID-19 patients in Japan: a simple clinical score for evaluating the need for hospitalisation

Predicting the need for hospitalisation of patients with coronavirus disease 2019 (COVID-19) is important for preventing healthcare disruptions. This observational study aimed to use the COVID-19 Registry Japan (COVIREGI-JP) to develop a simple scoring system to predict respiratory failure due to COVID-19 using only underlying diseases and symptoms. A total of 6873 patients with COVID-19 admitted to Japanese medical institutions between 1 June 2020 and 2 December 2020 were included and divided into derivation and validation cohorts according to the date of admission. We used multivariable logistic regression analysis to create a simple risk score model, with respiratory failure as the outcome for young (18-39 years), middle-aged (40-64 years) and older (≥65 years) groups, using sex, age, body mass index, medical history and symptoms. The models selected for each age group were quite different. Areas under the receiver operating characteristic curves for the simple risk score model were 0.87, 0.79 and 0.80 for young, middle-aged and elderly derivation cohorts, and 0.81, 0.80 and 0.67 in the validation cohorts. Calibration of the model was good. The simple scoring system may be useful in the appropriate allocation of medical resources during the COVID-19 pandemic.

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity

Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.

COVID-19 and HIV/AIDS in a cohort study in Sao Paulo, Brazil: outcomes and disparities by race and schooling

Studies describing characteristics and outcomes of COVID-19 among people living with HIV are currently limited, lacking detailed evaluation of the interplay among demographics, HIV-related variables, and comorbidities on COVID-19 outcomes. This retrospective cohort study describes mortality rates overall and according to demographic characteristics and explores predictors of admission to intensive care unit and death among 255 persons living with HIV with severe acute respiratory syndrome and confirmed SARS-CoV-2 infection in the State of Sao Paulo, Brazil. We found that the overall mortality rate was 4.1/1,000 person-days, with a case-fatality of 34%. Higher rates occurred among older adults, Black/Mixed skin color/race patients, and those with lower schooling. In a multivariable analysis adjusted for age, sex, CD4 count, viral load and number of comorbidities, skin color/race, and schooling remained significantly associated with higher mortality. Although tenofovir use was more frequent among survivors in the univariable analysis, we failed to find a statistically significant association between tenofovir use and survival in the multivariable analysis. Our findings suggest that social vulnerabilities related to both HIV and COVID-19 significantly impact the risk of death, overtaking traditional risk factors such as age, sex, CD4 count, and comorbidities.

Risk Factors Associated With COVID-19 Outcomes Among People With Intellectual and Developmental Disabilities Receiving Residential Services

IMPORTANCE

Although there is evidence of more severe COVID-19 outcomes, there is no information describing the risk factors for COVID-19 diagnosis and/or mortality among people with intellectual and developmental disabilities (IDD) receiving residential support services in the US.

OBJECTIVE

To identify associations between demographic characteristics, residential characteristics, and/or preexisting health conditions and COVID-19 diagnosis and mortality for people with IDD receiving residential support services.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study tracked COVID-19 outcomes for 543 individuals with IDD. Participants were receiving support services from a single organization providing residential services in the 5 boroughs of New York City from March 1 to October 1, 2020. Statistical analysis was performed from December 2020 to February 2021.

EXPOSURES

Resident-level characteristics, including age, sex, race/ethnicity, disability status, residential characteristics, and preexisting medical conditions.

MAIN OUTCOMES AND MEASURES

COVID-19 diagnosis was confirmed by laboratory test. COVID-19 mortality indicated that the individual died from COVID-19 during the course of the study. Logistic regression models were used to evaluate associations between demographic characteristics, residential characteristics, and preexisting health conditions and COVID-19 diagnosis and mortality.

RESULTS

Among the 543 individuals with IDD in the study, the median (interquartile range) age was 57.0 (45-65) years; 217 (40.0%) were female, and 274 (50.5%) were Black, Asian/Pacific Islander, American Indian or Alaskan Native, or Hispanic. The case rate was 16 759 (95% CI, 13 853-20 131) per 100 000; the mortality rate was 6446 (95% CI, 4671-8832) per 100 000; and the case-fatality rate was 38.5% (95% CI, 29.1%-48.7%). Increased age (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Down syndrome (OR, 2.91; 95% CI, 1.49-5.69), an increased number of residents (OR, 1.07; 95% CI, 1.00-1.14), and chronic kidney disease (OR, 4.17; 95% CI, 1.90-9.15) were associated with COVID-19 diagnosis. Heart disease (OR, 10.60; 95% CI, 2.68-41.90) was associated with COVID-19 mortality.

CONCLUSIONS AND RELEVANCE

This study found that, similar to the general population, increased age and preexisting health conditions were associated with COVID-19 outcomes for people with IDD receiving residential support services in New York City. As with older adults living in nursing homes, number of residents was also associated with more severe COVID-19 outcomes. Unique to people with IDD was an increased risk of COVID-19 diagnosis for people with Down syndrome.

External validation and update of prediction models for unfavorable outcomes in hospitalized patients with COVID-19 in Japan

Introduction

Most of the currently used prognostic models for COVID-19 are based on Western cohorts, but it is unknown whether any are applicable to patients with COVID-19 in Japan.

Methods

This retrospective cohort study included 160 patients with COVID-19 who were admitted to the National Center for Global Health and Medicine between January 26, 2020 and July 25, 2020. We searched PubMed for prognostic models for COVID-19. The predicted outcome was initiation of respiratory support or death. Performance of the candidate models was evaluated according to discrimination and calibration. We recalibrated the intercept of each model with our data. We also updated each model by adding β2-microglobulin (β2MG) to the model and recalculating the intercept and the coefficient of β2MG.

Results

Mean patient age was 49.8 years, 68% were male, 88.7% were Japanese. The study outcomes occurred in 15 patients, including two deaths. Two-hundred sixty-nine papers were screened, and four candidate prognostic models were assessed. The model of Bartoletti et al. had the highest area under receiver operating characteristic curve (AUC) (0.88; 95% confidence interval 0.81–0.96). All four models overestimated the probability of occurrence of the outcome. None of the four models showed statistically significant improvement in AUCs by adding β2MG.

Conclusions

Our results suggest that the existing prediction models for COVID-19 overestimate the probability of occurrence of unfavorable outcomes in a Japanese cohort. When applying a prediction model to a different cohort, it is desirable to evaluate its performance according to the prevalent health situation in that region.

Development and validation of a predictive model to determine the level of care in patients confirmed with COVID-19

BACKGROUND

The COVID-19 pandemic has imposed significant challenges on hospital capacity. While mitigating unnecessary crowding in hospitals is favourable to reduce viral transmission, it is more important to prevent readmissions with impaired clinical status due to initially inappropriate level of care. A validated predictive tool to assist clinical decisions for patient triage and facilitate remote stratification is of critical importance.

METHODS

We conducted a retrospective study in patients with confirmed COVID-19 stratified into two levels of care, namely ambulatory care and hospitalization. Data on socio-demographics, clinical symptoms, and comorbidities were collected during the first (N = 571) and second waves (N = 174) of the pandemic in Belgium (2 March to 6 December 2020). Univariate and multivariate logistic regressions were performed to build and validate the prediction model.

RESULTS

Significant predictors of hospitalization were old age (OR = 1.08, 95%CI:1.06-1.10), male gender (OR = 4.41, 95%CI: 2.58-7.52), dyspnoea (OR 6.11, 95%CI: 3.58-10.45), dry cough (OR 2.89, 95%CI: 1.54-5.41), wet cough (OR 4.62, 95%CI: 1.93-11.06), hypertension (OR 2.20, 95%CI: 1.17-4.16) and renal failure (OR 5.39, 95%CI: 1.00-29.00). Rhinorrhea (OR 0.43, 95%CI: 0.24-0.79) and headache (OR 0.36, 95%CI: 0.20-0.65) were negatively associated with hospitalization. A receiver operating characteristic (ROC) curve was constructed and the area under the ROC curve was 0.931 (95% CI: 0.910-0.953) for the prediction model (first wave) and 0.895 (95% CI: 0.833-0.957) for the validated dataset (second wave).

CONCLUSION

With a good discriminating power, the prediction model might identify patients who require ambulatory care or hospitalization and support clinical decisions by Emergency Department staff and general practitioners.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.