External validation of the COVID-19 4C mortality score in an urban United States cohort

External validation of six COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

OBJECTIVES

To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing homes.

STUDY DESIGN AND SETTING

This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation.

MAIN OUTCOME MEASURE

In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting.

RESULTS

All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large -1.45 to 7.46, calibration slopes 0.24-0.81, and C-statistic 0.55-0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of -2.35 to -0.15 indicating overestimation, calibration slopes of 0.24-0.81 indicating signs of overfitting, and C-statistic of 0.55-0.71.

CONCLUSION

Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Physiologic Scoring Systems in Predicting the COVID-19 Patients' one-month Mortality; a Prognostic Accuracy Study

Introduction : It is critical to quickly and easily identify severe coronavirus disease 2019 (COVID-19) patients and predict their mortality. This study aimed to determine the accuracy of the physiologic scoring systems in predicting the mortality of COVID-19 patients. Methods: This prospective cross-sectional study was performed on COVID-19 patients admitted to the emergency department (ED). The clinical characteristics of the participants were collected by the emergency physicians and the accuracy of the Quick Sequential Failure Assessment (qSOFA), Coronavirus Clinical Characterization Consortium (4C) Mortality, National Early Warning Score-2 (NEWS2), and Pandemic Respiratory Infection Emergency System Triage (PRIEST) scores for mortality prediction was evaluated. Results: Nine hundred and twenty-one subjects were included. Of whom, 745 (80.9%) patients survived after 30 days of admission. The mean age of patients was 59.13 ± 17.52 years, and 550 (61.6%) subjects were male. Non-Survived patients were significantly older (66.02 ± 17.80 vs. 57.45 ± 17.07, P< 0.001) and had more comorbidities (diabetes mellitus, respiratory, cardiovascular, and cerebrovascular disease) in comparison with survived patients. For COVID-19 mortality prediction, the AUROCs of PRIEST, qSOFA, NEWS2, and 4C Mortality score were 0.846 (95% CI [0.821-0.868]), 0.788 (95% CI [0.760-0.814]), 0.843 (95% CI [0.818-0.866]), and 0.804 (95% CI [0.776-0.829]), respectively. All scores were good predictors of COVID-19 mortality. Conclusion: All studied physiologic scores were good predictors of COVID-19 mortality and could be a useful screening tool for identifying high-risk patients. The NEWS2 and PRIEST scores predicted mortality in COVID-19 patients significantly better than qSOFA.

Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study

OBJECTIVES

To develop a disease stratification model for COVID-19 that updates according to changes in a patient's condition while in hospital to facilitate patient management and resource allocation.

DESIGN

In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.

SETTING

All data used in this study were obtained from a single UK teaching hospital.

PARTICIPANTS

We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.

RESULTS

Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).

CONCLUSIONS

Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient's clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.

TRIAL REGISTRATION

The study is registered as 'researchregistry5464' on the Research Registry (www.researchregistry.com).