BMC Infect Dis . Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

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BMC Infect Dis


. 2021 Sep 25;21(1):1004.
doi: 10.1186/s12879-021-06656-w.
Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases


Rahmet Guner ¹ , Bircan Kayaaslan ² , Imran Hasanoglu ¹ , Adalet Aypak ³ , Hurrem Bodur ⁴ , Ihsan Ates ⁵ , Esragul Akinci ⁴ , Deniz Erdem ⁶ , Fatma Eser ¹ , Seval Izdes ⁷ , Ayse Kaya Kalem ¹ , Aliye Bastug ⁴ , Aysegul Karalezli ⁸ , Aziz Ahmet Surel ⁹ , Muge Ayhan ³ , Selma Karaahmetoglu ⁵ , Isıl Ozkocak Turan ¹⁰ , Emine Arguder ⁷ , Burcu Ozdemir ³ , Mehmet Nevzat Mutlu ⁶ , Yesim Aybar Bilir ³ , Elif Mukime Sarıcaoglu ³ , Derya Gokcinar ¹⁰ , Sibel Gunay ¹¹ , Bedia Dinc ¹² , Emin Gemcioglu ⁵ , Ruveyda Bilmez ³ , Omer Aydos ³ , Dilek Asilturk ³ , Osman Inan ⁵ , Turan Buzgan ¹



Affiliations

• PMID: 34563117
• DOI: 10.1186/s12879-021-06656-w

Free article

Abstract

Background: Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values.
Methods: Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis.
Results: Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703).
Conclusion: We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.

Keywords: COVID-19; Intensive Care; Nomogram; Predictive Factors; Severity Score.