Dataset Information

ABC₂-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

ABSTRACT:

Objectives

The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.

Methods

Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients.

Results

Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO₂/FiO₂ ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/).

Conclusions

An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19.

SUBMITTER: Marcolino MS

PROVIDER: S-EPMC8302820 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ABC<sub>2</sub>-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

Marcolino Milena S MS Pires Magda C MC Ramos Lucas Emanuel F LEF Silva Rafael T RT Oliveira Luana M LM Carvalho Rafael L R RLR Mourato Rodolfo Lucas S RLS Sánchez-Montalvá Adrián A Raventós Berta B Anschau Fernando F Chatkin José Miguel JM Nogueira Matheus C A MCA Guimarães-Júnior Milton H MH Vietta Giovanna G GG Duani Helena H Ponce Daniela D Ziegelmann Patricia K PK Castro Luís C LC Ruschel Karen B KB Cimini Christiane C R CCR Francisco Saionara C SC Floriani Maiara A MA Nascimento Guilherme F GF Farace Bárbara L BL Monteiro Luanna S LS Souza-Silva Maira V R MVR Sales Thais L S TLS Martins Karina Paula M P KPMP Borges do Nascimento Israel J IJ Fereguetti Tatiani O TO Ferrara Daniel T M O DTMO Botoni Fernando A FA Etges Ana Paula B S APBS Schwarzbold Alexandre V AV Maurílio Amanda O AO Scotton Ana Luiza B A ALBA Weber André P AP Costa André S M ASM Glaeser Andressa B AB Madureira Angélica Aparecida C AAC Bhering Angelinda R AR de Castro Bruno Mateus BM da Silva Carla Thais C A CTCA Ramos Carolina M CM Gomes Caroline D CD de Carvalho Cíntia A CA Silveira Daniel V DV Cezar Edilson E Pereira Elayne C EC Kroger Emanuele Marianne S EMS Vallt Felipe B FB Lucas Fernanda B FB Aranha Fernando G FG Bartolazzi Frederico F Crestani Gabriela P GP Bastos Gisele A N GAN Madeira Glícia Cristina de C GCC Noal Helena Carolina HC Vianna Heloisa R HR Guimarães Henrique C HC Gomes Isabela M IM Molina Israel I Batista Joanna d'Arc L JDL de Alvarenga Joice C JC Guimarães Júlia D S S JDSS de Morais Júlia D P JDP Rugolo Juliana M JM Pontes Karen Cristina J R KCJR Dos Santos Kauane Aline M KAM de Oliveira Leonardo S LS Pinheiro Lílian S LS Pacheco Liliane S LS Sousa Lucas de D LD Couto Luciana S F LSF Kopittke Luciane L de Moura Luis Cesar S LCS Santos Luisa Elem A LEA Cabral Máderson A S MAS Souza Maíra D MD Tofani Marcela G T MGT Carneiro Marcelo M Ferreira Maria Angélica P MAP Bicalho Maria Aparecida C MAC Lima Maria Clara P B MCPB Godoy Mariana F MF Cardoso Marilia M A MMA Figueiredo Meire P MP Sampaio Natália C S NCS Rangel Natália L NL Crespo Natália T NT de Oliveira Neimy R NR Assaf Pedro L PL Martelli Petrônio José de L PJL Almeida Rafaela S C RSC Martins Raphael C RC Lutkmeier Raquel R Valacio Reginaldo Aparecido RA Finger Renan G RG Cardoso Ricardo B RB Pozza Roberta R Campos Roberta X RX Menezes Rochele M RM de Abreu Roger M RM Silva Rufino de F RF Guimarães Silvana M M SMM Araújo Silvia F SF Pereira Susany Anastácia SA Oliveira Talita F TF Kurtz Tatiana T de Oliveira Thainara C TC Araújo Thaíza Simônia M A TSMA Diniz Thulio Henrique O THO Dos Santos Veridiana B VB Gomes Virginia Mara R VMR do Vale Vitor Augusto L VAL Ramires Yuri C YC Boersma Eric E Polanczyk Carisi A CA

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 20210724

<h4>Objectives</h4>The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.<h4>Methods</h4>Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Lo ...[more]

PMID: 34311100

Similar Datasets

Project description:BackgroundPredicting the need for invasive mechanical ventilation (IMV) is important for the allocation of human and technological resources, improvement of surveillance, and use of effective therapeutic measures. This study aimed (i) to assess whether the ABC2-SPH score is able to predict the receipt of IMV in COVID-19 patients; (ii) to compare its performance with other existing scores; (iii) to perform score recalibration, and to assess whether recalibration improved prediction.MethodsRetrospective observational cohort, which included adult laboratory-confirmed COVID-19 patients admitted in 32 hospitals, from 14 Brazilian cities. This study was conducted in two stages: (i) for the assessment of the ABC2-SPH score and comparison with other available scores, patients hospitalized from July 31, 2020, to March 31, 2022, were included; (ii) for ABC2-SPH score recalibration and also comparison with other existing scores, patients admitted from January 1, 2021, to March 31, 2022, were enrolled. For both steps, the area under the receiving operator characteristic score (AUROC) was calculated for all scores, while a calibration plot was assessed only for the ABC2-SPH score. Comparisons between ABC2-SPH and the other scores followed the Delong Test recommendations. Logistic recalibration methods were used to improve results and adapt to the studied sample.ResultsOverall, 9,350 patients were included in the study, the median age was 58.5 (IQR 47.0-69.0) years old, and 45.4% were women. Of those, 33.5% were admitted to the ICU, 25.2% received IMV, and 17.8% died. The ABC2-SPH score showed a significantly greater discriminatory capacity, than the CURB-65, STSS, and SUM scores, with potentialized results when we consider only patients younger than 80 years old (AUROC 0.714 [95% CI 0.698-0.731]). Thus, after the ABC2-SPH score recalibration, we observed improvements in calibration (slope = 1.135, intercept = 0.242) and overall performance (Brier score = 0.127).ConclusionThe ABC2-SPHr risk score demonstrated a good performance to predict the need for mechanical ventilation in COVID-19 hospitalized patients under 80 years of age.

Project description:BackgroundIdentification of patients with novel coronavirus disease 2019 (COVID-19) requiring hospital admission or at high-risk of in-hospital mortality is essential to guide patient triage and to provide timely treatment for higher risk hospitalized patients.MethodsA retrospective multi-centre (8 hospital) cohort at Beaumont Health, Michigan, USA, reporting on COVID-19 patients diagnosed between 1 March and 1 April 2020 was used for score validation. The COVID-19 Risk of Complications Score was automatically computed by the EHR. Multivariate logistic regression models were built to predict hospital admission and in-hospital mortality using individual variables constituting the score. Validation was performed using both discrimination and calibration.ResultsCompared to Green scores, Yellow Scores (OR: 5.72) and Red Scores (OR: 19.1) had significantly higher odds of admission (both p < .0001). Similarly, Yellow Scores (OR: 4.73) and Red Scores (OR: 13.3) had significantly higher odds of in-hospital mortality than Green Scores (both p < .0001). The cross-validated C-Statistics for the external validation cohort showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)).ConclusionsThe COVID-19 Risk of Complications Score predicts the need for hospital admission and in-hospital mortality patients with COVID-19. Key points: Can an electronic health record generated risk score predict the risk of hospital admission and in-hospital mortality in patients diagnosed with coronavirus disease 2019 (COVID-19)? In both validation cohorts of 2,025 and 1,290 COVID-19, the cross-validated C-Statistics showed good discrimination for both hospital admission (C = 0.79 (95% CI: 0.77-0.81)) and in-hospital mortality (C = 0.75 (95% CI: 0.71-0.78)), respectively. The COVID-19 Risk of Complications Score may help predict the need for hospital admission if a patient contracts SARS-CoV-2 infection and in-hospital mortality for a hospitalized patient with COVID-19.

Project description:BackgroundThe prevalence of and mortality from HIV-associated tuberculosis (HIV/TB) in hospital inpatients in Africa remains unacceptably high. Currently, there is a lack of tools to identify those at high risk of early mortality who may benefit from adjunctive interventions. We therefore aimed to develop and validate a simple clinical risk score to predict mortality in high-burden, low-resource settings.Methods and findingsA cohort of HIV-positive adults with laboratory-confirmed TB from the STAMP TB screening trial (Malawi and South Africa) was used to derive a clinical risk score using multivariable predictive modelling, considering factors at hospital admission (including urine lipoarabinomannan [LAM] detection) thought to be associated with 2-month mortality. Performance was evaluated internally and then externally validated using independent cohorts from 2 other studies (LAM-RCT and a Médecins Sans Frontières [MSF] cohort) from South Africa, Zambia, Zimbabwe, Tanzania, and Kenya. The derivation cohort included 315 patients enrolled from October 2015 and September 2017. Their median age was 36 years (IQR 30-43), 45.4% were female, median CD4 cell count at admission was 76 cells/μl (IQR 23-206), and 80.2% (210/262) of those who knew they were HIV-positive at hospital admission were taking antiretroviral therapy (ART). Two-month mortality was 30% (94/315), and mortality was associated with the following factors included in the score: age 55 years or older, male sex, being ART experienced, having severe anaemia (haemoglobin < 80 g/l), being unable to walk unaided, and having a positive urinary Determine TB LAM Ag test (Alere). The score identified patients with a 46.4% (95% CI 37.8%-55.2%) mortality risk in the high-risk group compared to 12.5% (95% CI 5.7%-25.4%) in the low-risk group (p < 0.001). The odds ratio (OR) for mortality was 6.1 (95% CI 2.4-15.2) in high-risk patients compared to low-risk patients (p < 0.001). Discrimination (c-statistic 0.70, 95% CI 0.63-0.76) and calibration (Hosmer-Lemeshow statistic, p = 0.78) were good in the derivation cohort, and similar in the external validation cohort (complete cases n = 372, c-statistic 0.68 [95% CI 0.61-0.74]). The validation cohort included 644 patients between January 2013 and August 2015. Median age was 36 years, 48.9% were female, and median CD4 count at admission was 61 (IQR 21-145). OR for mortality was 5.3 (95% CI 2.2-9.5) for high compared to low-risk patients (complete cases n = 372, p < 0.001). The score also predicted patients at higher risk of death both pre- and post-discharge. A simplified score (any 3 or more of the predictors) performed equally well. The main limitations of the scores were their imperfect accuracy, the need for access to urine LAM testing, modest study size, and not measuring all potential predictors of mortality (e.g., tuberculosis drug resistance).ConclusionsThis risk score is capable of identifying patients who could benefit from enhanced clinical care, follow-up, and/or adjunctive interventions, although further prospective validation studies are necessary. Given the scale of HIV/TB morbidity and mortality in African hospitals, better prognostic tools along with interventions could contribute towards global targets to reduce tuberculosis mortality.

Dataset Information

ABC₂-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

Objectives

Methods

Results

Conclusions

Publications

ABC<sub>2</sub>-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

ABC2-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

Objectives

Methods

Results

Conclusions

Publications

ABC<sub>2</sub>-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

ABC₂-SPH risk score for in-hospital mortality in COVID-19 patients: development, external validation and comparison with other available scores.