A nomogramic model based on clinical and laboratory parameters at admission for predicting the survival of COVID-19 patients

Abstract

Background COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission. Methods COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson’s χ2-test or Fisher’s exact test, and continuous variables were analyzed using Student’s t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method. Results A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868–0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097–0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041–1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003–1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973–0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990–0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617–0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923–0.973). Conclusions A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.

Epidemiologic Characteristics of and Prognostic Factors for COVID-19 Among Hospitalized Patients: Updated Implications From Hubei Province, China

“…Regarding other laboratory indicators with significance in univariate analyses in our study for which there was supporting evidence from national and international studies, WBC count ( 30 , 33 , 39 , 42 – 45 ), percentage of lymphocytes ( 8 ), PLT count ( 8 , 33 , 42 , 46 – 48 ), CRP level ( 8 , 33 , 43 , 46 , 48 , 49 ), neutrophil count ( 33 , 42 , 43 , 48 ), ALB level ( 33 , 43 , 49 ), blood UN ( 9 , 33 ), and blood glucose level ( 45 ) were also associated with prognosis. However, some significant laboratory parameters, such as lactate dehydrogenase ( 7 , 8 , 33 , 44 , 45 ), D-dimer ( 7 , 8 , 10 , 33 , 43 ), and procalcitonin ( 5 , 33 , 44 , 50 ), were not investigated in our study owing to a lack of data.…”

“…Because China was the first country to report the outbreak of COVID-19, Chinese clinicians and researchers have continued to explore and update the assessment of epidemic characteristics and COVID-19 prognostic factors, providing evidence for its prevention, diagnosis, and treatment. The prognostic factors include demographic characteristics (5), underlying diseases (6), laboratory parameters (7,8), clinical manifestations (9,10), radiological features (11), and treatment (12). In addition, vaccination becomes a positive prognostic factor recently, with more than 10 vaccines of different strategies being reported to reduce the severity of the disease (13)(14)(15)(16)(17).…”

Serum hydroxybutyrate dehydrogenase and COVID-19 severity and mortality: a systematic review and meta-analysis with meta-regression

“…Nineteen studies were retrospective [ 38 , 39 , 41 – 47 , 49 – 55 , 57 – 59 ], whilst the remaining three did not report the study design [ 40 , 48 , 56 ]. Clinical endpoints included disease severity based on current clinical guidelines (13 studies) [ 39 , 41 , 43 , 45 – 48 , 50 – 52 , 55 , 59 ], disease progress (two studies) [ 40 , 56 ], occurrence of ARDS (one study) [ 54 ], and survival (six studies) [ 38 , 42 , 44 , 49 , 57 , 58 ]. In all studies, serum HBDH concentrations were measured on admission.…”

Peran Parameter Trombosit sebagai Biomarker Keparahan dan Prognosis Pasien Coronavirus Disease 2019

“…Berdasarkan 6 artikel yang telah di-review, hasil penelitian 5 artikel menunjukkan bahwa hitung jumlah trombosit dapat menilai tingkat keparahan dan prognosis pada pasien COVID-19 serta 1 artikel menunjukkan bahwa nilai MPV tidak berperan sebagai prediktor yang baik untuk tingkat keparahan penyakit COVID-19. Hasil penelitian menunjukkan terdapat 5 artikel yang membuktikan bahwa parameter trombosit yang dapat dijadikan sebagai biomarker keparahan dan prognosis pasien penyakit Coronavirus 2019 adalah jumlah trombosit [9]- [12], [14].…”

“…Berdasarkan outcome pasien COVID-19, pada penelitian Xiaojun et al, terdapat perbedaan hitung jumlah trombosit yang signifikan antara kelompok survivors dan non-survivors [14]. Dalam penelitian oleh Zhou et al, median hitung jumlah trombosit secara signifikan lebih rendah pada non-survivors dibandingkan dengan survivors.…”