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Table 4 Prediction models fit to UIH test cohort

From: Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models

Study

Cohort origin

N (develop)

Outcome

Method

Covariates

Performance

Test cohort # evaluable

Test cohort performance

A] An interpretable mortality prediction model for COVID-19 patients [42]

Wuhan, China

351

Mortality*

Decision tree

(3) CRP, LDH, lymphocyte percentage

PPV 96.9%

NPV 98.4%

145 (70%)***

AUC 0.69 (0.60–0.79)

B] Development and validation of a clinical risk score to predict the occurrence of critical illness of hospitalized patients with COVID-19 [19]a

China

1590

Criticality**

Logistic regression (LR)

(10) Age, cancer, direct bilirubin, comorbidities, dyspnea, hemoptysis, LDH, neutrophils/lymphocytes, unconscious, CXR

AUC 0.88 (0.84–0.93)

144 (70%)

AUC 0.84 (0.78–0.91)

C] Development and validation of prognosis model of mortality risk in patients with COVID-19 [20]

Wuhan, China

292

Mortality

LR

(3) Age, LDH, CRP

AUC 0.95

141 (69%)

AUC 0.89 (0.82–0.96)

D] Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients [26]

Wuhan, China

305

Mortality

LR

(2) BUN, D-dimer

AUC 0.94 (0.90–0.97)

150 (72%)

AUC 0.73 (0.62–0.83)

E] Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: a retrospective study [28]

Wuhan, China

336

Mortality

LR

(3) BUN, D-dimer, lymphocyte percentage

AUC 0.99 (0.98–1.0)

148 (71%)***

AUC 0.72 (0.61–0.82)

F] Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19 [44]

Wuhan, China

299

Mortality

LR

(4) Age, LDH, lymphocyte count, O2 Saturation

AUC 0.98 (0.96–1.0)

150 (72%)

AUC 0.84 (0.73–0.94)

G] Early prediction of mortality risk among severe COVID-19 patients using machine learning [46]b

Wuhan, China

183

Mortality

LR

(4) Age, CRP, D-dimer, lymphocyte count

AUC 0.90

142 (69%)

AUC 0.68 (0.51–0.81)

H] Risk prediction for poor outcome and death in hospital in-patients with COVID19: derivation in Wuhan, China and external validation in London, UK [(51)]c

Wuhan, China

775

Mortality

LR

(7) Age, CRP, sex, Cr, lymphocytes, neutrophils, platelets count

AUC 0.91

165 (80%)

AUC 0.72 (0.58–0.86)

UIH mortality model

Chicago, USA

309

Mortality

Random forest

(11) Age, AST, BMI, Cr, CRP, diastolic BP, ferritin, O2 saturation, platelet count, RDW, WBC

AUC 0.98 (0.96–1.0)

152 (73%)

AUC 0.84 (0.74–0.94)

UIH criticality model

Chicago, USA

309

Criticality

Random forest

(11) Age, ALT, AST, Cr, CRP, ferritin, RDW, neutrophils/lymphocytes, O2 saturation, platelet count, WBC

AUC 0.97 (0.94–1.0)

152 (73%)

AUC 0.83 (0.76–0.90)

  1. ALT, alanine aminotransferase; AST, aspartate transaminase; BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; Cr, creatinine; CRP, C reactive peptide; CXR, chest Xray; LDH, lactate dehydrogenase; O2, oxygen; PC, procalcitonin; RDW, red blood cell distribution width; SAT, oxygen saturation; WBC, white blood cell count
  2. *Mortality defined as death prior to discharge**Criticality defined as mortality or intensive care unit stay
  3. ***For these models only non-pregnant patients were used. The other models either included or did not specify inclusion of pregnant patients
  4. ahttp://118.126.104.170/
  5. bhttps://phenomics.fudan.edu.cn/risk_scores/
  6. chttps://covid.datahelps.life/prediction/