A total of 4330 patients were admitted to the ICU between 2010 and 2016 and, of these, 32 patients were diagnosed with cardiac arrest on the ICU. Thirteen out of the 32 patients were excluded due to insufficient data and age <18 years old. Finally, 19 patients were included in the present study and 12,050 data sets representing vital signs were recorded. 12,038 data sets were enrolled in the present study and 12 data set were manually excluded due to artifact or measurement error made by clinician judgment. In the all data set, 9,915 out of 12,038 (82.3%) data set of vital signs were allocated to VS and 2,111 of 12,038 (17.7%) data set were allocated to TS. 80% of each data set, randomly selected 7932 data set from VS and 1,689 data set from TS were allocated to the development data set, respectively. Furthermore, in the development set, 6,243 randomly selected VS data sets out of a total of 7932 were used for non-linear regression analysis and 1,689 from each development data set (VS and TS) were used for determination of threshold. Also, 20% of each data set, 1,983 data set from VS and 422 data set from TS were allocated to the validation set. (Figure. 1) Subject demographic characteristics were expressed as median and interquartile range, as follows: age 63 years (53 to 75 years), sex (men 15/19, 79%), duration of ICU stay 3 days (1 to 8 days), duration of hospital stay 7 days (3 to 23 days). The diagnoses were categorized as follows: 5 cardiac surgical(26%), 4 surgical (21%), 5 medical (26%), and 5 cancer (26%).
Median and interquartile range of residual time to death from the point when blood pressure was <80 mmHg was 279 minutes (126 to 864) across all data. Non-linear regression with an exponential decay model showed a fitted curve for the association between shock index and SBP. The exponential decay model is widely used in the natural sciences. 8 In the development set, from the 6,243 variables, exponential decay was derived from the following equation.
γ = Plateau + (γ0- Plateau) e-kx
The parameters of the equation were as follows: Y0=6.931 (6.638 to 7.224), plateau=0.995 (0.926 to 1.063), K=0.035(0.327 to 0.037). The following equation was derived:
Predicted Shock Index = 0.995 +(6.931-0.995) e -0.035 x Systolic Blood Pressure (Figure. 2)
R square is 0.699.
Median and interquartile range of the disparity in the two data sets are 1.90 (1.1 to 2.9) for the volatile data set and -9.8 (-10.9 to -8.5) for the terminal data set, respectively. Area under the ROC of the new prediction model in the development set was 0.650 (0.512 to 0.788), p<0.001, and the threshold of disparity between real and predicted heart rate was -10 bpm with sensitivity of 49.9%, specificity of 75.8%, and likelihood ratio of 2.06. (Figure. 3)
In the validation set, the predictive power of the new prediction model for cardiac arrest conducted in the development set was analyzed. In the validation data set, the diagnostic power was as follows: sensitivity 52.7 % (47.8 to 57.6), specificity 79.8 % (78.0 to 81.6), positive predictive value 35.7 % (31.9 to 39.6), negative predictive value 88.8 % (87.2 to 90.3), and likelihood ratio 2.61.
In the secondary outcome analysis, we determined a new formula for the accurate prediction of the residual time to cardiac arrest. Residual time to cardiac arrest was prescribed as the dependent variable. Age, sex, heart rate, respiratory rate, and SBP were prescribed as explanatory variables, measured within 120 minutes from cardiac arrest. Diastolic blood pressure and mean blood pressure were excluded, due to the strong correlation with SBP. The following formula was determined using the linear regression model:
Predicted Residual Time to Cardiac Arrest= 12.3 + (0.18 x Age) + (Male: -6.0, Female: 0) + (0.28 x Systolic Blood Pressure) + (0.34 x Heart Rate) + (-0.25 x Respiratory Rate) (Table. 1)
Residual time to cardiac arrest with linear regression model analyzed with vital data within 120 minutes from cardiac arrest in the development set.
Residual standard error: 28.87 on 1287 degrees of freedom Multiple R-squared: 0.3167, Adjusted R-squared: 0.314; F-statistic: 119.3 on 5 and 1287 degrees of freedom, p-value: < 2.2e-16
In the validation set, the disparity between real residual time to cardiac arrest and predicted residual time to cardiac arrest calculated with the linear regression model was expressed as mean (3.6 minutes), median (-1.49 minutes), and interquartile range (-22.2 to 31.4 minutes).