Development and validation of a prognostic nomogram for predicting in-hospital mortality of COVID-19: a multicenter retrospective cohort study of 4086 cases in China

Abstract

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.

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

“…Therefore, LDH may help detecting tissue damage in the onset of COVID-19 infection. Similar to our study, some previous studies reported LDH as a predictive marker for severe illness [ 20 , 24 , 27 , 31 , 32 ]. Troponin I is also one of the predictors in our nomogram.…”

“…In the literature, there are studies proposing nomograms and/or different models to predict serious illness or death in COVID-19 patients [ 19 , 21 , 22 , 24 , 25 , 35 – 37 ]. Age was reported as a predictor for severe COVID-19 or mortality in previous studies and is included in most of the prediction models [ 16 – 21 , 24 , 25 , 32 , 35 , 38 , 39 ]. We found an increased risk of severe infection requiring ICU admission in the patients 56.5 years of age and older.…”

“…The association between comorbid diseases and the development of severe infection was indicated in previous studies [ 14 , 16 , 17 , 31 , 41 , 42 ]. Some were severity and mortality risk scores [ 16 , 19 , 22 , 31 , 32 , 37 ]. Although we found that comorbid diseases were more frequent in severe cases requiring ICU follow up than the patients without ICU need, comorbidities were not identified as optimal predictors during the development process of the nomogram.…”

Post-sequelae one year after hospital discharge among older COVID-19 patients: A multi-center prospective cohort study

“…Totally included in this multi-center prospective cohort study were real-time reverse transcription polymerase chain reaction (RT-PCR)-confirmed older COVID-19 patients (age ≥ 60) who were discharged from Huoshenshan Hospital and Taikang-Tongji Hospital (both in Wuhan, China) between Feb 12 and Apr 10, 2020. 8 All discharged patients met the uniform discharge criteria of the World Health Organization interim guidance. 27 The follow-up was conducted between Mar 1, 2021 and Mar 20, 2021.…”

“…5 Both the occurrence and prognosis of COVID-19 were closely related to older age. [6][7][8] To date, series of studies have reported the potential short-to long-term sequelae of COVID-19 recovery, ranging from two months to one year. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] However, the older patients have not attracted enough attention and no specific studies quantified the temporal trends and associated risk factors for sequelae of COVID-19 in older patients, especially long-term sequelae.…”