Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: results from the IMPACC study

BackgroundBetter understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management. MethodsImmunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1,164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed. FindingsThe median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age [≥] 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63-4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC. InterpretationIntegration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19. FundingNIH RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSWe did a systematic search of the PubMed database from January 1st, 2020 until April 24th, 2022 using the search terms: "hospitalized" AND "SARS-CoV-2" OR "COVID-19" AND "Pro-spective" AND "Antibody" OR "PCR" OR "long term follow up" and applying the following filters: "Multicenter Study" AND "Observational Study". No language restrictions were applied. While clinical, laboratory, and radiographic features associated with severe COVID-19 in hospitalized adults have been described, description of the kinetics of SARS-CoV-2 specific assays available to clinicians (e.g. PCR and binding antibody) and their integration with other variables is scarce for both short and long term follow up. The current literature is comprised of several studies with small sample size, cross-sectional design with laboratory data typically only recorded at a single point in time (e.g., on admission), limited clinical characteristics, variable duration of follow up, single-center setting, retrospective analyses, kinetics of either PCR or antibody testing but not both, and outcomes such as death or, mechanical ventilation that do not allow delineation of variations in clinical course. Added value of this studyIn our large longitudinal multicenter cohort, the description of outcome severity, was not limited to survival versus death, but encompassed a clinical trajectory approach leveraging longitudinal data based on time in hospital, disease severity by ordinal scale based on degree of respiratory illness, and presence or absence of limitations at discharge. Fatal COVID-19 cases had the lowest ratio of antibody to viral load levels over time as compared to non-fatal cases. Integration of PCR cycle threshold and antibody values with demographics, baseline comorbidities, and laboratory/radiographic findings identified additional risk factors for outcome severity over the first 28 days. However, female sex was the only variable associated with persistence of symptoms over time. Persistence of symptoms was not associated with clinical trajectory over the first 28 days, nor with antibody/viral loads from the acute phase. Implications of all the available evidenceThe described calculated ratio (binding IgG/PCR Ct value) is unique compared to other studies, reflecting host pathogen interactions and representing an accessible approach for patient risk stratification. Integration of SARS-CoV-2 viral load and binding antibody kinetics with other laboratory as well as clinical characteristics in hospitalized COVID-19 patients can identify patients likely to have the most severe short-term outcomes, but is not predictive of symptom persistence at one year post-discharge.

Rapid emergence of SARS-CoV-2 Omicron variant is associated with an infection advantage over Delta in vaccinated persons

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continues to shape the coronavirus disease 2019 (Covid-19) pandemic. The detection and rapid spread of the SARS-CoV-2 Omicron variant (lineage B.1.1.529) in Botswana and South Africa became a global concern because it contained 15 mutations in the spike protein immunogenic receptor binding domain and was less neutralized by sera derived from vaccinees compared to the previously dominant Delta variant. To investigate if Omicron is more likely than Delta to cause infections in vaccinated persons, we analyzed 37,877 nasal swab PCR tests conducted from 12-26 December 2021 and calculated the test positivity rates for each variant by vaccination status. We found that the positivity rate among unvaccinated persons was higher for Delta (5.2%) than Omicron (4.5%). We found similar results in persons who received a single vaccine dose. Conversely, our results show that Omicron had higher positivity rates than Delta among those who received two doses within five months (Omicron = 4.7% vs. Delta = 2.6%), two doses more than five months ago (4.2% vs. 2.9%), and three vaccine doses (2.2% vs. 0.9%). Our estimates of Omicron positivity rates in persons receiving one or two vaccine doses were not significantly lower than unvaccinated persons but were 49.7% lower after three doses. In comparison, the reduction in Delta positivity rates from unvaccinated to 2 vaccine doses was 45.6-49.6% and to 3 vaccine doses was 83.2%. Despite the higher positivity rates for Omicron in vaccinated persons, we still found that 91.2% of the Omicron infections in our study occurred in persons who were eligible for 1 or more vaccine doses at the time of PCR testing. In conclusion, escape from vaccine-induced immunity likely contributed to the rapid rise in Omicron infections.

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

ObjectiveReal-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations. MethodsElectronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations. ResultsOf the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19. ConclusionsCOVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection

ObjectiveSevere acute respiratory syndrome virus (SARS-CoV-2) has infected millions of people worldwide. Our goal was to identify risk factors associated with admission and disease severity in patients with SARS-CoV-2. DesignThis was an observational, retrospective study based on real-world data for 7,995 patients with SARS-CoV-2 from a clinical data repository. SettingYale New Haven Health (YNHH) is a five-hospital academic health system serving a diverse patient population with community and teaching facilities in both urban and suburban areas. PopulationsThe study included adult patients who had SARS-CoV-2 testing at YNHH between March 1 and April 30, 2020. Main outcome and performance measuresPrimary outcomes were admission and in-hospital mortality for patients with SARS-CoV-2 infection as determined by RT-PCR testing. We also assessed features associated with the need for respiratory support. ResultsOf the 28605 patients tested for SARS-CoV-2, 7995 patients (27.9%) had an infection (median age 52.3 years) and 2154 (26.9%) of these had an associated admission (median age 66.2 years). Of admitted patients, 1633 (75.8%) had a discharge disposition at the end of the study period. Of these, 192 (11.8%) required invasive mechanical ventilation and 227 (13.5%) expired. Increased age and male sex were positively associated with admission and in-hospital mortality (median age 81.9 years), while comorbidities had a much weaker association with the risk of admission or mortality. Black race (OR 1.43, 95%CI 1.14-1.78) and Hispanic ethnicity (OR 1.81, 95%CI 1.50-2.18) were identified as risk factors for admission, but, among discharged patients, age-adjusted in-hospital mortality was not significantly different among racial and ethnic groups. ConclusionsThis observational study identified, among people testing positive for SARS-CoV-2 infection, older age and male sex as the most strongly associated risks for admission and in-hospital mortality in patients with SARS-CoV-2 infection. While minority racial and ethnic groups had increased burden of disease and risk of admission, age-adjusted in-hospital mortality for discharged patients was not significantly different among racial and ethnic groups. Ongoing studies will be needed to continue to evaluate these risks, particularly in the setting of evolving treatment guidelines.

Development and validation of the COVID-19 severity index (CSI): a prognostic tool for early respiratory decompensation

ObjectiveThe goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19. DesignObservational, retrospective cohort study. SettingNine-hospital health system within the Northeastern United States. PopulationsAdult patients ([≥] 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for respiratory critical illness within 4 hours of arrival were excluded. Main outcome and performance measuresWe used a composite endpoint of critical illness as defined by oxygen requirement (greater than 10 L/min by low-flow device, high-flow device, non-invasive, or invasive ventilation) or death within the first 24 hours of hospitalization. We developed models predicting our composite endpoint using patient demographic and clinical data available within the first four hours of arrival. Eight hospitals (n = 932) were used for model development and one hospital (n = 240) was held out for external validation. Area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC), and calibration metrics were used to compare predictive models to three illness scoring systems: Elixhauser comorbidity index, qSOFA, and CURB-65. ResultsDuring the study period from March 1, 2020 to April 27,2020, 1,792 patients were admitted with COVID-19. Six-hundred and twenty patients were excluded based on age or critical illness within the first 4 hours, yielding 1,172 patients in the final cohort. Of these patients, 144 (12.3%) met the composite endpoint within the first 24 hours. We first developed a bedside quick COVID-19 severity index (qCSI), a twelve-point scale using nasal cannula flow rate, respiratory rate, and minimum documented pulse oximetry. We then created a machine-learning gradient boosting model, the COVID-19 severity index (CSI), using twelve additional variables including inflammatory markers and liver chemistries. Both the qCSI (AU-ROC mean [95% CI]: 0.90 [0.85-0.96]) and CSI (AU-ROC: 0.91 [0.86-0.97]) outperformed the comparator models (qSOFA: 0.76 [0.69-0.85]; Elixhauser: 0.70 [0.62-0.80]; CURB-65: AU-ROC 0.66 [0.58-0.77]) on cross-validation and performed well on external validation (qCSI: 0.82, CSI: 0.76, CURB-65: 0.50, qSOFA: 0.59, Elixhauser: 0.61). We find that a qCSI score of 0-3 is associated with a less than 5% risk of critical respiratory illness, while a score of 9-12 is associated with a 57% risk of progression to critical illness. ConclusionsA significant proportion of admitted COVID-19 patients decompensate within 24 hours of hospital presentation and these events are accurately predicted using bedside respiratory exam findings within a simple scoring system.