Exploiting an early warning nomogram for predicting the risk of ICU admission in COVID-19 patients: A multi-center study in China

Abstract

Background Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).Methods Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.Results The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.Conclusion We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Score Tomográfico de Severidad (TSS) como predictor de admisión a Unidad de Cuidados Intensivos (UCI) en pacientes con neumonía COVID-19 en una clínica de San Juan de Lurigancho Lima, Perú

“…Encontramos que los factores asociados más comunes con hospitalización en UCI fueron: edad, IMC, hipertensión, diabetes mellitus, TSS, dímero D, ferritina elevada, lo cual es consistente con datos previos que demuestran un mayor riesgo de peores resultados en estos pacientes. [15] La leucocitosis fue otro factor asociado [16] , que también fue demostrado en algunos estudios, La literatura temprana también ha propuesto que un conteo de los neutrófilos absoluto/relativo, elevado puede ser predictivo de enfermedad crítica en esta población [17,18] , aunque se describe a la linfopenia y el RDW alto como predictores de mortalidad [19] , en el presente estudio, solo la linfopenia estuvo asociada a la admisión a UCI. [20][21][22] los pacientes graves y las formas leves de la enfermedad podrían deberse en parte a comorbilidades respiratorias o cardíacas.…”

Analyzing the Death Ratio of Covid Patients using Multiple Logistic Regression in Comparison with Lasso Regression for Improving Accuracy

“…Another article has a citation of 11 and which is cited in Springer and it discusses how simple-to-use is used to predict deterioration and and survival of patients with covid19 (Zeng et al 2021). One more article (Zhou et al 2020) has 8 citations and is cited in Springer and discusses exploiting risks of covid19 patients joining in hospitals. One more article (Laguna-Goya et al 2020) has a citation of 150.…”

Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study

“…The Coronavirus Disease of 2019 (COVID-19) is a highly contagious viral infection that has to propagate speedily around the world as of the first advent in early December 2019 reported in Wuhan, Hubei province, China ( 1 , 2 ). The COVID-19 is characterized by a varied and multi-dimensional clinical picture.…”