Dataset Information

SARS-CoV-2 infection: Initial viral load (iVL) predicts severity of illness/outcome, and declining trend of iVL in hospitalized patients corresponds with slowing of the pandemic.

ABSTRACT:

Background

Hospitalization of patients infected with the severe acute respiratory syndrome virus 2 (SARS-CoV-2) have remained considerable worldwide. Patients often develop severe complications and have high mortality rates. The cycle threshold (Ct) value derived from nasopharyngeal swab samples using real time polymerase chain reaction (RT-PCR) may be a useful prognostic marker in hospitalized patients with SARS-CoV-2 infection, however, its role in predicting the course of the pandemic has not been evaluated thus far.

Methods

We conducted a retrospective cohort study which included all patients who had a nasopharyngeal sample positive for SARS-CoV-2 between April 4 -June 5, 2020. The Ct value was used to estimate the number of viral particles in a patient sample. The trend in initial viral load on admission on a population level was evaluated. Moreover, patient characteristics and outcomes stratified by viral load categories were compared and initial viral load was assessed as an independent predictor of intubation and in-hospital mortality.

Results

A total of 461 hospitalized patients met the inclusion criteria. This study consisted predominantly of acutely infected patients with a median of 4 days since symptom onset to PCR. As the severity of the pandemic eased, there was an increase in the percentage of samples in the low initial viral load category, coinciding with a decrease in deaths. Compared to an initial low viral load, a high initial viral load was an independent predictor of in-hospital mortality (OR 5.5, CI 3.1-9.7, p < 0.001) and intubation (OR 1.82 CI 1.07-3.11, p = 0.03), while an initial intermediate viral load was associated with increased risk of inpatient mortality (OR 1.9, CI 1.14-3.21, p = 0.015) but not with increased risk for intubation.

Conclusion

The Ct value obtained from nasopharyngeal samples of hospitalized patients on admission may serve as a prognostic marker at an individual level and may help predict the course of the pandemic when evaluated at a population level.

SUBMITTER: El Zein S

PROVIDER: S-EPMC8445469 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivesTo validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care.DesignDeep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database.SettingHospitals with pediatric routine inpatient and ICU care.PatientsChildren cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data.InterventionsNone.Measurements and main resultsA calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations.ConclusionsThe Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

Project description:ImportanceThe measured severity of illness of hospitalized Medicare beneficiaries has increased. Whether this change is associated with payment reforms, concentrated among hospitalizations with principal diagnoses targeted by payment reform, and reflective of true increases in severity of illness is unknown.ObjectivesTo assess whether the expansion of secondary diagnosis codes in January 2011 and the incentive payments for health information technology under the US Health Information Technology for Economic and Clinical Health Act were associated with changes in measured severity of illness and whether those changes are reflective of true increases in underlying patient severity.Design, setting, and participantsThis cohort study of Medicare fee-for-service beneficiary discharges (N = 47 951 443) between January 1, 2008, and August 31, 2015, used a regression-discontinuity design to evaluate changes in measured severity of illness after the expansion of secondary diagnoses. Discharge-level linear regression model with hospital fixed effects was used to evaluate changes in measured severity of illness after hospitals' receipt of incentives for health information technology. The change in predictive accuracy of measured severity of illness on 30-day readmissions after the implementation of both policies was evaluated. Data analysis was performed from November 1, 2018, to March 5, 2019.Main outcomes and measuresThe primary outcome was patients' measured severity of illness determined by the number of condition categories from secondary discharge diagnosis codes. Measured severity of illness for diagnoses commonly targeted by Medicare policies and untargeted diagnoses was assessed.ResultsIn total, 47 951 443 discharges at 2850 hospitals were included. In 2008, these beneficiaries included 3 882 672 women (58.5%) with a mean (SD) age of 78.5 (8.4) years. In 2014, the discharges included 3 377 137 women (57.8%) with the mean (SD) age of 78.4 (8.7) years. The Centers for Medicare & Medicaid Services expansion of secondary diagnoses was associated with a 0.348 (95% CI, 0.328-0.367; P < .001) change in condition categories for all diagnoses, 0.445 (95% CI, 0.419-0.470; P < .001) for targeted diagnoses, and 0.321 (95% CI, 0.302-0.341; P < .001) for untargeted diagnoses. Health information technology incentives were associated with a 0.013 (95% CI, 0.004-0.022; P = .005) change in condition categories for all diagnoses, 0.195 (95% CI, 0.184-0.207; P < .001) for targeted diagnoses, and -0.016 (95% CI, -0.025 to -0.007; P < .001) for untargeted diagnoses. Minimal improvements in predictive accuracy were observed.Conclusions and relevanceChanges in Centers for Medicare & Medicaid Services policies appear to be associated with increases in measured severity of illness; these increases do not appear to reflect substantive changes in true patient severity.