Establishment of Routine Clinical Indicators-Based Nomograms for Predicting the Mortality in Patients With COVID-19

Abstract

This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

“…Fifty-five studies aimed to exclusively predict in-hospital mortality [ 2 , 11 , 14 , 15 , 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 29 , 30 , 31 , 32 , 34 , 35 , 36 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 47 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 79 ], while thirteen reported on combined outcome...…”

“…Among nomograms developed from hospital records, the derivation sample size was small (less than 300 patients) in 24 studies [ 2 , 12 , 14 , 17 , 25 , 27 , 28 , 30 , 31 , 32 , 33 , 34 , 35 , 37 , 40 , 42 , 47 , 48 , 55 , 67 , 73 , 78 , 79 ] and relatively small (300 to 500 patients) in 14 studies [ 11 , 13 , 16 , 18 , 20 , 22 , 39 , 41 , 44 , 51 , 60 , 74 , 75 , 80 ]. Only 20 hospital-based studies derived mortality prediction rules from series over 1000 patients [ 21 , 24 , 26 , 36 , 38 , 43 , 50 , 52 , 54 , 59 , 62 , 63 , 65 , ...…”

“…Thirty-four nomograms were derived and validated (in different patients from the same institution) in the same study; random training test splits [ 14 , 17 , 18 , 19 , 23 , 24 , 30 , 32 , 33 , 41 , 45 , 50 , 51 , 55 , 59 , 60 , 61 , 62 , 63 , 65 , 66 , 67 , 71 , 75 , 76 , 77 ] and temporal splits [ 15 , 43 , 44 , 46 , 53 , 70 ] were performed in fourteen and five studies, respectively. One study performed validation by leave-one-hospital-out cross validation [ 72 ].…”

“…Only 12 studies externally validated their clinical prediction tool in a cohort of patients in a different institution [ 11 , 13 , 26 , 38 , 48 , 52 , 55 , 65 , 66 , 70 , 74 , 75 ] from which it was derived.…”

“…Overall, 67 models identified predictors for risk of COVID-19-related mortality in patients admitted to hospitals, and a further 3 also included information from the general population and/or out-patients [ 19 , 23 , 49 , 71 , 77 ]. The vast majority of these studies used multivariable logistic regression models to identify predictors [ 2 , 12 , 13 , 14 , 16 , 17 , 18 , 20 , 21 , 22 , 24 , 25 , 26 , 27 , 30 , 31 , 32 , 33 , 34 , 35 , 37 , 38 , 39 , 41 , 43 , 45 , 46 , 47 , 48 , 49 , 51 , 52 , 53 , 54 , 56 , 57 , 58 , 59 , 62 , 63 , 64 , 65 , 66 ,…”