Prospective predictive performance comparison between Clinical Gestalt and validated COVID-19 mortality scores

ABSTRACT Background Most COVID-19 mortality scores were developed in the early months of the pandemic and now available evidence-based interventions have helped reduce its lethality. It has not been evaluated if the original predictive performance of these scores holds true nor compared it against Clinical Gestalt predictions. We tested the current predictive accuracy of six COVID-19 scores and compared it with Clinical Gestalt predictions.Methods 200 COVID-19 patients were enrolled in a tertiary hospital in Mexico City between September and December 2020. Clinical Gestalt predictions of death (as a percentage) and LOW-HARM, qSOFA, MSL-COVID-19, NUTRI-CoV and NEWS2 were obtained at admission. We calculated the AUC of each score and compared it against Clinical Gestalt predictions and against their respective originally reported value.Results 106 men and 60 women aged 56+/-9 and with confirmed COVID-19 were included in the analysis. The observed AUC of all scores was significantly lower than originally reported; LOW-HARM 0.96 (0.94-0.98) vs 0.76 (0.69-0.84), qSOFA 0.74 (0.65-0.81) vs 0.61 (0.53-0.69), MSL-COVID-19 0.72 (0.69-0.75) vs 0.64 (0.55-0.73) NUTRI-CoV 0.79 (0.76-0.82) vs 0.60 (0.51-0.69), NEWS2 0.84 (0.79-0.90) vs 0.65 (0.56-0.75), Neutrophil-Lymphocyte ratio 0.74 (0.62-0.85) vs 0.65 (0.57-0.73). Clinical Gestalt predictions were non-inferior to mortality scores (AUC=0.68 (0.59-0.77)). Adjusting the LOW-HARM score with locally derived likelihood ratios did not improve its performance. However, some scores performed better than Clinical Gestalt predictions when clinician’s confidence of prediction was <80%.Conclusion No score was significantly better than Clinical Gestalt predictions. Despite its subjective nature, Clinical Gestalt has relevant advantages for predicting COVID-19 clinical outcomes..

Medienart:

Preprint

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

bioRxiv.org - (2023) vom: 02. Nov. Zur Gesamtaufnahme - year:2023

Sprache:

Englisch

Beteiligte Personen:

Soto-Mota, Adrian [VerfasserIn]
Marfil-Garza, Braulio A. [VerfasserIn]
de Obeso, Santiago Castiello [VerfasserIn]
Martínez, Erick [VerfasserIn]
Carrillo-Vázquez, Daniel Alberto [VerfasserIn]
Tadeo-Espinoza, Hiram [VerfasserIn]
Guerrero-Cabrera, Jessica Paola [VerfasserIn]
Dardón-Fierro, Francisco Eduardo [VerfasserIn]
Escobar Valderrama, Juan Manuel [VerfasserIn]
Alanis-Mendizabal, Jorge [VerfasserIn]
Gutiérrez, Juan [VerfasserIn]

Links:

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Themen:

570
Biology

doi:

10.1101/2021.04.16.21255647

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XBI020376073