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Additional information
Methods:We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients aged >18 years using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). All patients were diagnosed by positive tests of novel coronavirus nucleic acids (SARS-CoV-2), according to WHO interim guidance. External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C-statistic, and calibration using calibration-in-the-large, calibration slopes and plots.
Findings:
The final model included age, lymphocyte count, lactate dehydrogenase and SpO2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data.
Source:
Preprint article by Xie et al, 2020 avaialble at MedRxiv.
Study Population
Total population size: 299Continuous characteristics
| Name | LL | Q1 | Median | Q3 | UL | Unit |
|---|---|---|---|---|---|---|
| Age | 54 | 65 | 73 | years | ||
| Days since onset | 7 | 10 | 14 | years | ||
| SpO2 | 90 | 95 | 98 | % | ||
| SOFA score | 2 | 2 | 4 | points | ||
| Systolic blood pressure | 118 | 132.5 | 145 | mmHg | ||
| White blood cell count | 4.7 | 6.8 | 10.6 | 10^9/L | ||
| Lymphocyte count | 0.50 | 0.75 | 1.11 | 10^9/L | ||
| Platelet count | 137.3 | 179.5 | 247.8 | 10^9/L | ||
| ALT | 16 | 26 | 40.8 | U/L | ||
| AST | 24 | 33.5 | 56 | U/L | ||
| Bilirubin | 7.6 | 10.3 | 14.6 | umol/L | ||
| BUN | 3.9 | 5.9 | 9.2 | mmol/L | ||
| Creatinine | 58 | 74 | 95 | umol/l | ||
| Creatinine kinase | 70 | 131 | 380.8 | U/L | ||
| LDH | 264 | 384 | 541 | U/L | ||
| CRP | 27.4 | 65.3 | 113.9 | mg/dl | ||
| INR | 1.04 | 1.13 | 1.25 | ratio | ||
| Procalcitonine | 0.05 | 0.13 | 0.37 | ng/ml |
Categorical characteristics
| Name | Subset / Group | Nr. of patients |
|---|---|---|
| Hypertension | yes | 127 |
| no | 172 | |
| Diabetes | yes | 55 |
| no | 244 | |
| COPD | yes | 15 |
| no | 284 | |
| Heart failure | yes | 13 |
| no | 286 | |
| Smoking | yes | 10 |
| no | 289 | |
| Other co-morbidities | yes | 19 |
| no | 280 | |
| SpO2 | ≥90% | 229 |
| <90% | 67 | |
| D-dimer | <0.5 mg/L | 49 |
| 0.5-1.0 mg/L | 55 | |
| ≥1.0 mg/L | 154 | |
| cTnI | <28.0 pg/ml | 173 |
| ≥28.0 pg/ml | 69 |
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prognostic multivariable model on admission for hospitalized patients with COVID-19 |
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V-1.29-2117.20.04.13 |
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Refer to Intended Use for instructions before use |
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Evidencio B.V., Irenesingel 19, 7481 GJ, Haaksbergen, the Netherlands |
Related files
| Preview | Name | Tags |
|---|---|---|
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Xie et al, 2020 (preprint).pdf 901.12 kB |
External validation Internal validation Paper Model coefficients Risk factors Patient characteristics |
.png)
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Model performance on internal and external validation (Xie et al, 2020).png 71.58 kB |
Figure (results-page) |
Supporting Publications
| Title or description | Tags |
|---|---|
| Preprint Xie et al, 2020 | External validation Internal validation Paper Model coefficients Risk factors Patient characteristics Tripod |
Predicted probability of mortality: ...
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Predicted probability of mortality:
Outcome stratification
Conditional information
Result interpretation
Intended use:
There is an urgent need for effective prediction models to identify patients who are most likely to develop respiratory failure and poor outcomes. This model was developed to predict mortality of COVID-19 hospitalized patients to guide decision-making between clinicians, patients and families.
Model performance:
The model has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c-statistic=0.89) and external (c=0.98) validation. When applied to new patients, the model yielded probabilities of mortality that were too high for low risk patients and too low for high risk patients (calibration slope >1). After recalibration of the model to account for underlying differences in the risk profile of the datasets, calibration plots showed excellent agreement between predicted and observed probabilities of mortality.
Model limitations:
Recruited patients were mainly self-referrals, who are more likely to have severe symptoms that cause them to seek emergency medical help. Other hospitals are more likely to test patients who have more pronounced symptoms, thus creating an inherent bias in the patient sample. Therefore, the cohort may not accurately represent patients with mild or asymptomatic COVID-19. These results need to be further validated with patients who are hospitalized at different locations and for different severities of illness to confirm generalizability and robustness of the model. Finally, the accompanying publication describing the development and validation of the model has not yet been peer reviewed.
References:
Preprint article by Xie et al, 2020 avaialble at MedRxiv.
Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.
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