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Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients.

ABSTRACT: Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.

SUBMITTER: Werfel S

PROVIDER: S-EPMC8426905 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients.

Werfel Stanislas S Jakob Carolin E M CEM Borgmann Stefan S Schneider Jochen J Spinner Christoph C Schons Maximilian M Hower Martin M Wille Kai K Haselberger Martina M Heuzeroth Hanno H Rüthrich Maria M MM Dolff Sebastian S Kessel Johanna J Heemann Uwe U Vehreschild Jörg J JJ Rieg Siegbert S Schmaderer Christoph C

Journal of medical virology 20210810 12

Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total o ...[more]

PMID: 34331717

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Project description:BackgroundMultiple myeloma (MM) is the second most common hematological malignancy, and the treatments markedly elevate the survival rate of the patients in recent years. However, the prevalence of cardiovascular adverse events (CVAEs) in MM had been increasing recently. CVAEs in MM patients are an important problem that we should focus on. Clinical tools for prognostication and risk-stratification are needed.Patients and methodsThis is a retrospective study that included patients who were newly diagnosed with multiple myeloma (NDMM) in Shanghai Changzheng Hospital and Affiliated Jinhua Hospital, Zhejiang University School of Medicine from June 2018 to July 2020. A total of 253 patients from two medical centers were divided into training cohort and validation cohort randomly. Univariable analysis of the baseline factors was performed using CVAEs endpoints. Multivariable analysis identified three factors for a prognostic model that was validated in internal validation cohorts.ResultsFactors independently associated with CVAEs in NDMM were as follows: age>61 years old, high level of baseline office blood pressure, and left ventricular hypertrophy (LVH). Age contributed 2 points, and the other two factors contributed 1 point to a prognostic model. The model distinguished the patients into three groups: 3-4 points, high risk; 2 points, intermediate risk; 0-1 point, low risk. These groups had significant difference in CVAEs during follow-up days in both training cohort (p<0.0001) and validation cohort (p=0.0018). In addition, the model had good calibration. The C-indexes for the prediction of overall survival of CVAEs in the training and validation cohorts were 0.73 (95% CI, 0.67-0.79) and 0.66 (95% CI, 0.51-0.81), respectively. The areas under the receiver operating characteristic curve (AUROCs) of the 1-year CVAEs probability in the training and validation cohorts were 0.738 and 0.673, respectively. The AUROCs of the 2-year CVAE probability in the training and validation cohorts were 0.722 and 0.742, respectively. The decision-curve analysis indicated that the prediction model provided greater net benefit than the default strategies of providing assessment or not providing assessment for all patients.ConclusionA prognostic risk prediction model for predicting CVAEs risk of NDMM patients was developed and internally validated. Patients at increased risk of CVAEs can be identified at treatment initiation and be more focused on cardiovascular protection in the treatment plan.

Project description:ImportanceEarly identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.ObjectiveTo develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China.Design, setting, and participantsCollaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.Main outcomes and measuresAmong patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death.ResultsThe development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/).Conclusions and relevanceIn this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.

Project description:IntroductionThis study aimed at generating a new simplified prognostic score (SPS) using common clinical and biological variables to discriminate a limited number of subgroups of patients with SCLC differing by their overall survival (OS).MethodsThe SPS was developed exploring the Montpellier University Hospital retrospective database of 401 patients over a 16-year period. All patients had received etoposide - platinum-based chemotherapy as first-line treatment. The SPS development took into account significant determinants of OS in the Cox model, weighted by their regression β coefficients. Validation of the consequent SPS has been done separately in a combined population of 213 patients accrued from two different published trials (NCT03059667 and NCT00930891).ResultsThe significant independent determinants of OS included the following: (1) American Joint Committee on Cancer TNM stage IV (hazard ratio [HR]: 2.52; 95% confidence interval [CI]: 1.91-3.33); (2) Eastern Cooperative Oncology Group performance status greater than 1 (HR: 2.27; 95% CI: 1.79-2.87); (3) the presence of liver metastases (HR: 1.66; 95% CI: 1.29-2.15); and (4) neutrophil-to-lymphocyte ratio greater than 4 (HR: 1.39; 95% CI: 1.11-1.92). The SPS generated with these four variables, segregated three groups (good, intermediate, and poor prognosis) with respective median OS of 26.9 months (95% CI: 20.1-38.9), 11.5 months (95% CI: 9.8-13.0), and 6.8 months (95% CI: 5.8-8.3; log-rank p < 10-4). Harrell's C statistic estimate was 0.68 ± 0.012, suggesting goodness of calibration. In the validation cohort, the SPS segregated the aforementioned three subgroups in a nearly similar manner, with respective median OS: 27.2, 12.3, and 8.6 months (log-rank p < 10-3; Harrell's C statistic: 0.58 ± 0.02).ConclusionsThe SPS is easy to calculate in real-life practice and efficiently discriminates three populations with different prognoses. This study deserves further validation of this score in patients with SCLC receiving immunochemotherapy.

Project description:ObjectivesICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission.DesignProspective cohort study in seven Dutch ICUs.Setting/patientsICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020.InterventionsNone.Measurements and main resultsOutcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80).ConclusionsThe developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

Project description:ObjectiveThe objective of this study was to develop valid prognostic models to predict mortality, dependency, and "death or dependency" for use in newly diagnosed Parkinson's disease (PD).MethodsThe models were developed in the Parkinsonism Incidence in North-East Scotland study (UK, 198 patients) and validated in the ParkWest study (Norway, 192 patients), cohorts that attempted to identify and follow-up all new PD cases in the study area. Dependency was defined using the Schwab & England scale. We selected variables measured at time of diagnosis to include in the models. Internal validation and external validation were performed by calculating C-statistics (discrimination) and plotting observed versus predicted risk in quantiles of predicted risk (calibration).ResultsOlder age, male sex, increased severity of axial features, and Charlson comorbidity index were independent prognostic factors in the mortality model. Increasing age, higher smoking history, increased severity of axial features, and lower MMSE score were independent predictors in the models of dependency and "death or dependency." Each model had very good internal calibration and very good or good discrimination (internal and external C-statistics for the models were 0.73-0.75 and 0.68-0.78, respectively). Although each model clearly separated patients into groups according to risk, they tended to overestimate risk in ParkWest. The models were recalibrated to the baseline risk in the ParkWest study and then calibrated well in this cohort.ConclusionsWe have developed prognostic models for predicting medium-term risk of important clinical outcomes in newly diagnosed PD. These models have validity for use for stratification of randomization, confounder adjustment, and case-mix correction, but they are inadequate for individualized prognostication. © 2017. The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Dataset Information

Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients.

Publications

Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients.

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