Correction: Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

[This corrects the article DOI: 10.2196/35190.].

Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS).

BACKGROUND: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. OBJECTIVE: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. METHODS: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. RESULTS: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than the knowledge-based adjustment set for 3 reference drugs: dexamethasone, phloroglucinol, and paracetamol. After correction for multiple testing, 4 drugs were associated with increased in-hospital mortality. Among these, diazepam and tramadol were the only ones not discarded by automated diagnostics, with adjusted odds ratios of 2.51 (95% CI 1.52-4.16, Q=.1) and 1.94 (95% CI 1.32-2.85, Q=.02), respectively. CONCLUSIONS: Our innovative approach proved useful in generating pharmacological hypotheses in an outbreak setting, without requiring a priori knowledge of the disease. Our systematic analysis of early prescribed treatments from patients hospitalized for COVID-19 showed that diazepam and tramadol are associated with increased 28-day mortality. Whether these drugs could worsen COVID-19 needs to be further assessed.

Adjusting D-dimer to Lung Disease Extent to Exclude Pulmonary Embolism in COVID-19 Patients (Co-LEAD).

OBJECTIVE: D-dimer measurement is a safe tool to exclude pulmonary embolism (PE), but its specificity decreases in coronavirus disease 2019 (COVID-19) patients. Our aim was to derive a new algorithm with a specific D-dimer threshold for COVID-19 patients. METHODS: We conducted a French multicenter, retrospective cohort study among 774 COVID-19 patients with suspected PE. D-dimer threshold adjusted to extent of lung damage found on computed tomography (CT) was derived in a patient set (n = 337), and its safety assessed in an independent validation set (n = 337). RESULTS: According to receiver operating characteristic curves, in the derivation set, D-dimer safely excluded PE, with one false negative, when using a 900 ng/mL threshold when lung damage extent was <50% and 1,700 ng/mL when lung damage extent was ≥50%. In the derivation set, the algorithm sensitivity was 98.2% (95% confidence interval [CI]: 94.7-100.0) and its specificity 28.4% (95% CI: 24.1-32.3). The negative likelihood ratio (NLR) was 0.06 (95% CI: 0.01-0.44) and the area under the curve (AUC) was 0.63 (95% CI: 0.60-0.67). In the validation set, sensitivity and specificity were 96.7% (95% CI: 88.7-99.6) and 39.2% (95% CI: 32.2-46.1), respectively. The NLR was 0.08 (95% CI; 0.02-0.33), and the AUC did not differ from that of the derivation set (0.68, 95% CI: 0.64-0.72, p = 0.097). Using the Co-LEAD algorithm, 76 among 250 (30.4%) COVID-19 patients with suspected PE could have been managed without CT pulmonary angiography (CTPA) and 88 patients would have required two CTs. CONCLUSION: The Co-LEAD algorithm could safely exclude PE, and could reduce the use of CTPA in COVID-19 patients. Further prospective studies need to validate this strategy.

Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of university hospitals.

The SARS-Cov2 may have impaired care trajectories, patient overall survival (OS), tumor stage at initial presentation for new colorectal cancer (CRC) cases. This study aimed at assessing those indicators before and after the beginning of the pandemic in France. In this retrospective cohort study, we collected prospectively the clinical data of the 11.4 million of patients referred to the Greater Paris University Hospitals (AP-HP). We identified new CRC cases between 1 January 2018 and 31 December 2020, and compared indicators for 2018-2019 to 2020. pTNM tumor stage was extracted from postoperative pathology reports for localized colon cancer, and metastatic status was extracted from CT-scan baseline text reports. Between 2018 and 2020, 3602 and 1083 new colon and rectal cancers were referred to the AP-HP, respectively. The 1-year OS rates reached 94%, 93% and 76% for new CRC patients undergoing a resection of the primary tumor, in 2018-2019, in 2020 without any Sars-Cov2 infection and in 2020 with a Sars-Cov2 infection, respectively (HR 3.78, 95% CI 2.1-7.1). For patients undergoing other kind of anticancer treatment, the percentages are 64%, 66% and 27% (HR 2.1, 95% CI 1.4-3.3). Tumor stage at initial presentation, emergency level of primary tumor resection, delays between the first multidisciplinary meeting and the first anticancer treatment did not differ over time. The SARS-Cov2 pandemic has been associated with less newly diagnosed CRC patients and worse 1-year OS rates attributable to the infection itself rather than to its impact on hospital care delivery or tumor stage at initial presentation.

What level of D-dimers can safely exclude pulmonary embolism in COVID-19 patients presenting to the emergency department?

OBJECTIVES: To identify which level of D-dimer would allow the safe exclusion of pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: This retrospective study was conducted on the COVID database of Assistance Publique - Hôpitaux de Paris (AP-HP). COVID-19 patients who presented at the ED of AP-HP hospitals between March 1 and May 15, 2020, and had CTPA following D-dimer dosage within 48h of presentation were included. The D-dimer sensitivity, specificity, and positive and negative predictive values were calculated for different D-dimer thresholds, as well as the false-negative and failure rates, and the number of CTPAs potentially avoided. RESULTS: A total of 781 patients (mean age 62.0 years, 53.8% men) with positive RT-PCR for SARS-Cov-2 were included and 60 of them (7.7%) had CTPA-confirmed PE. Their median D-dimer level was significantly higher than that of patients without PE (4,013 vs 1,198 ng·mL-1, p < 0.001). Using 500 ng·mL-1, or an age-adjusted cut-off for patients > 50 years, the sensitivity and the NPV were above 90%. With these thresholds, 17.1% and 31.5% of CTPAs could have been avoided, respectively. Four of the 178 patients who had a D-dimer below the age-adjusted cutoff had PE, leading to an acceptable failure rate of 2.2%. Using higher D-dimer cut-offs could have avoided more CTPAs, but would have lowered the sensitivity and increased the failure rate. CONCLUSION: The same D-Dimer thresholds as those validated in non-COVID outpatients should be used to safely rule out PE. KEY POINTS: • The median D-dimer level was significantly higher in COVID-19 patients with PE as compared to those without PE (4,013 ng·mL-1 vs 1,198 ng·mL-1 respectively, p < 0.001). • Using 500 ng·mL-1, or an age-adjusted D-dimer cut-off to exclude pulmonary embolism, the sensitivity and negative predictive value were above 90%. • Higher cut-offs would lead to a reduction in the sensitivity below 85% and an increase in the failure rate, especially for patients under 50 years.

Low-income neighbourhood was a key determinant of severe COVID-19 incidence during the first wave of the epidemic in Paris.

BACKGROUND: Previous studies have demonstrated that socioeconomic factors are associated with COVID-19 incidence. In this study, we analysed a broad range of socioeconomic indicators in relation to hospitalised cases in the Paris area. METHODS: We extracted 303 socioeconomic indicators from French census data for 855 residential units in Paris and assessed their association with COVID-19 hospitalisation risk. FINDINGS: The indicators most associated with hospitalisation risk were the third decile of population income (OR=9.10, 95% CI 4.98 to 18.39), followed by the primary residence rate (OR=5.87, 95% CI 3.46 to 10.61), rate of active workers in unskilled occupations (OR=5.04, 95% CI 3.03 to 8.85) and rate of women over 15 years old with no diploma (OR=5.04, 95% CI 3.03 to 8.85). Of note, population demographics were considerably less associated with hospitalisation risk. Among these indicators, the rate of women aged between 45 and 59 years (OR=2.17, 95% CI 1.40 to 3.44) exhibited the greatest level of association, whereas population density was not associated. Overall, 86% of COVID-19 hospitalised cases occurred within the 45% most deprived areas. INTERPRETATION: Studying a broad range of socioeconomic indicators using census data and hospitalisation data as a readily available and large resource can provide real-time indirect information on populations with a high incidence of COVID-19.

Differential association between inflammatory cytokines and multiorgan dysfunction in COVID-19 patients with obesity.

To investigate the mechanisms underlying the SARS-CoV-2 infection severity observed in patients with obesity, we performed a prospective study of 51 patients evaluating the impact of multiple immune parameters during 2 weeks after admission, on vital organs' functions according to body mass index (BMI) categories. High-dimensional flow cytometric characterization of immune cell subsets was performed at admission, 30 systemic cytokines/chemokines levels were sequentially measured, thirteen endothelial markers were determined at admission and at the zenith of the cytokines. Computed tomography scans on admission were quantified for lung damage and hepatic steatosis (n = 23). Abnormal BMI (> 25) observed in 72.6% of patients, was associated with a higher rate of intensive care unit hospitalization (p = 0.044). SARS-CoV-2 RNAaemia, peripheral immune cell subsets and cytokines/chemokines were similar among BMI groups. A significant association between inflammatory cytokines and liver, renal, and endothelial dysfunctions was observed only in patients with obesity (BMI > 30). In contrast, early signs of lung damage (ground-glass opacity) correlated with Th1/M1/inflammatory cytokines only in normal weight patients. Later lesions of pulmonary consolidation correlated with BMI but were independent of cytokine levels. Our study reveals distinct physiopathological mechanisms associated with SARS-CoV-2 infection in patients with obesity that may have important clinical implications.

Placental growth factor level in plasma predicts COVID-19 severity and in-hospital mortality.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory disease associated with vascular inflammation and endothelial injury. OBJECTIVES: To correlate circulating angiogenic markers vascular endothelial growth factor A (VEGF-A), placental growth factor (PlGF), and fibroblast growth factor 2 (FGF-2) to in-hospital mortality in COVID-19 adult patients. METHODS: Consecutive ambulatory and hospitalized patients with COVID-19 infection were enrolled. VEGF-A, PlGF, and FGF-2 were measured in each patient ≤48 h following admission. RESULTS: The study enrolled 237 patients with suspected COVID-19: 208 patients had a positive diagnostic for COVID-19, of whom 23 were mild outpatients and 185 patients hospitalized after admission. Levels of VEGF-A, PlGF, and FGF-2 significantly increase with the severity of the disease (P < .001). Using a logistic regression model, we found a significant association between the increase of FGF-2 or PlGF and mortality (odds ratio [OR] 1.11, 95% confidence interval [CI; 1.07-1.16], P < .001 for FGF-2 and OR 1.07 95% CI [1.04-1.10], P < .001 for PlGF) while no association were found for VEGF-A levels. Receiver operating characteristic curve analysis was performed and we identified PlGF above 30 pg/ml as the best predictor of in-hospital mortality in COVID-19 patients. Survival analysis for PlGF confirmed its interest for in-hospital mortality prediction, by using a Kaplan-Meier survival curve (P = .001) and a Cox proportional hazard model adjusted to age, body mass index, D-dimer, and C-reactive protein (3.23 95% CI [1.29-8.11], P = .001). CONCLUSION: Angiogenic factor PlGF is a relevant predictive factor for in-hospital mortality in COVID-19 patients. More than a biomarker, we hypothesize that PlGF blocking strategies could be a new interesting therapeutic approach in COVID-19.

Association of Antihypertensive Agents with the Risk of In-Hospital Death in Patients with Covid-19.

PURPOSE: The role of angiotensin receptor blockers (ARB), angiotensin-converting enzyme inhibitors (ACEi), or other antihypertensive agents in the case of Covid-19 remains controversial. We aimed to investigate the association between antihypertensive agent exposure and in-hospital mortality in patients with Covid-19. METHODS: We performed a retrospective multicenter cohort study on patients hospitalized between February 1 and May 15, 2020. All patients had been followed up for at least 30 days. RESULTS: Of the 8078 hospitalized patients for Covid-19, 3686 (45.6%) had hypertension and were included in the study. In this population, the median age was 75.4 (IQR, 21.5) years and 57.1% were male. Overall in-hospital 30-day mortality was 23.1%. The main antihypertensive pharmacological classes used were calcium channel blockers (CCB) (n=1624, 44.1%), beta-blockers (n=1389, 37.7%), ARB (n=1154, 31.3%), and ACEi (n=998, 27.1%). The risk of mortality was lower in CCB (aOR, 0.83 [0.70-0.99]) and beta-blockers (aOR, 0.80 [0.67-0.95]) users and non-significant in ARB (aOR, 0.88 [0.72-1.06]) and ACEi (aOR, 0.83 [0.68-1.02]) users, compared to non-users. These results remain consistent for patients receiving CCB, beta-blocker, or ARB as monotherapies. CONCLUSION: This large multicenter retrospective of Covid-19 patients with hypertension found a reduced mortality among CCB and beta-blockers users, suggesting a putative protective effect. Our findings did not show any association between the use of renin-angiotensin-aldosterone system inhibitors and the risk of in-hospital death. Although they need to be confirmed in further studies, these results support the continuation of antihypertensive agents in patients with Covid-19, in line with the current guidelines.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

BACKGROUND: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. OBJECTIVE: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). METHODS: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. RESULTS: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. CONCLUSIONS: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

Curative anticoagulation prevents endothelial lesion in COVID-19 patients.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has been associated with cardiovascular complications and coagulation disorders. OBJECTIVES: To explore the coagulopathy and endothelial dysfunction in COVID-19 patients. METHODS: The study analyzed clinical and biological profiles of patients with suspected COVID-19 infection at admission, including hemostasis tests and quantification of circulating endothelial cells (CECs). RESULTS: Among 96 consecutive COVID-19-suspected patients fulfilling criteria for hospitalization, 66 were tested positive for SARS-CoV-2. COVID-19-positive patients were more likely to present with fever (P = .02), cough (P = .03), and pneumonia at computed tomography (CT) scan (P = .002) at admission. Prevalence of D-dimer >500 ng/mL was higher in COVID-19-positive patients (74.2% versus 43.3%; P = .007). No sign of disseminated intravascular coagulation were identified. Adding D-dimers >500 ng/mL to gender and pneumonia at CT scan in receiver operating characteristic curve analysis significantly increased area under the curve for COVID-19 diagnosis. COVID-19-positive patients had significantly more CECs at admission (P = .008) than COVID-19-negative ones. COVID-19-positive patients treated with curative anticoagulant prior to admission had fewer CECs (P = .02) than those without. Interestingly, patients treated with curative anticoagulation and angiotensin-converting-enzyme inhibitors or angiotensin receptor blockers had even fewer CECs (P = .007). CONCLUSION: Curative anticoagulation could prevent COVID-19-associated coagulopathy and endothelial lesion.