Time-Varying Discrimination Accuracy of Longitudinal Biomarkers for the Prediction of Mortality Compared to Assessment at Fixed Time Point in Severe Burns Patients

Time-Varying Discrimination Accuracy of Longitudinal Biomarkers for the Prediction of Mortality Compared to Assessment at Fixed Time Point in Severe Burns Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Original research Time-Varying Discrimination Accuracy of Longitudinal Biomarkers for the Prediction of Mortality Compared to Assessment at Fixed Time Point in Severe Burns Patients Jaechul Yoon, Dohern Kym, Jun Hur, Jae Hee Won, Haejun Yim, Yong Suk Cho, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-27589/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jan, 2021 Read the published version in BMC Emergency Medicine → Version 1 posted You are reading this latest preprint version Abstract Background: The distribution of biomarkers over time is considered an indicator of disease formation and helps in early detection of disease, thus reducing the disease-related mortality. The ability of biomarkers to predict outcomes has been evaluated using conventional cross-sectional methods; therefore, we investigated the prognostic potential of longitudinal biomarkers over time. Methods: The patients aged > 18 y who were admitted within 24 h of the burn incident in the burn intensive care unit were enrolled. Longitudinal biomarkers, such as WBC, platelet count, lactate, creatinine, TB, and PT were retrieved from a clinical database warehouse. Time-dependent ROC curves using a cumulative/dynamic and incident/dynamic approach were employed to evaluate time-varying prognostic performance. Results: Total 2259 patients were included in this retrospective study and divided into the survival group and non-survival group. With AUC using the ID approach, platelets showed the highest c-index with 0.930 (0.919 ~ 0.941) as well as the highest during all time points. PT and creatinine show over 8 c-index with 0.862 (0.843 ~ 0.881) and 0.828 (0.809 ~ 0.848), respectively. Conclusions: The platelet count was the best prognostic marker. PT and creatinine also showed good overall diagnostic ability. Lactate is known as a strong predictor; however, it showed relatively poorer prognostic performance in burns. Critical Care & Emergency Medicine ROC time-varying discrimination mortality longitudinal burns Figures Figure 1 Figure 2 Backgroud Burns represent one of most devastating traumas and results in high morbidity and mortality. Thus, the prediction of adverse effects of interest or mortality using updated biomarkers that are measured routinely over time is an essential part of care in an intensive care unit (ICU). The distribution of biomarker over time is considered an indicator of disease formation and is helpful in the early detection of disease, thus reducing the disease-related mortality ( 1 ). Many biomarkers that are measured at a single time, such as at admission or an event, such as the development of acute kidney injury (AKI) or intervention, have been used to predict the outcome at multiple time points of interest. If accurate predictions are possible, they could provide clinical recommendation about the selection and timing of interventions and help in the initiation of specific preventive strategies and aggressive treatment for high-risk individuals. Further, it could reduce the cost, adverse effects, and unnecessary interventions in low-risk patients. Ultimately, the goal of the prognostic model with the use of biomarkers is to accurately predict the time of the event or to distinguish cases and controls in every situation. In addition, the disease state of an individual changes over time; therefore, prognostic information, such as updated biomarkers recorded during routine measurements, also change and can affect the performance of decision-making tools. The concept of accuracy of sensitivity and specificity is fundamental to clinical research and decision modeling. Recently, statistical methods have been developed to generalize these traditional cross-sectional accuracy concepts for the application to time-varying characteristics of disease states.( 2 ) In clinical practice, the ability biomarkers to predict the outcome have been evaluated using a conventional, cross-sectional method. Therefore, we investigated the prognostic potential of biomarkers routinely used in clinical practice over time and compared whether different biomarkers have varying prognostic accuracy at different times during treatment. Methods From February 2007 to December 2018, patients aged > 18 y who were admitted within 24 h of the burn incident at the burn intensive care unit (BICU) of Hangang Sacred Heart Hospital, Hallym University Medical Center were included in this retrospective study; all the patients underwent acute fluid resuscitation during the first 3 days after the burn. The indications of admission to BICU were as follows: 1) partial thickness burn of > 20% of total body surface area (TBSA) for adults and partial thickness burn of > 10% of the TBSA in patients aged > 65 years, 2) inhalation injury, 3) electrical burn, 4) preexisting medical disorder that could incur complications or affect mortality, and 5) concomitant trauma that could elevate the risk of morbidity or mortality. Clinical longitudinal data that were measured routinely and were known predictors, such as white blood cell (WBC), platelet count, lactate ( 3 ), creatinine, total bilirubin (TB), and prothrombin time (PT) were retrieved from a clinical database warehouse at the Hangang Sacred Heart Hospital. The study period was the stay in the BICU. When the biomarkers were measured several times each day, we recorded the poorest value recorded. We also noted demographic variables, such as age, sex, TBSA (calculated by surgeon using a modified Lund and Browder chart) ( 4 ), type of burn, length of BICU stay, and presence of inhalation injury ( 5 ). The primary outcome was the ICU mortality. The severity of injury was reported using the Abbreviated Burn Severity Index (ABSI) ( 6 ), the newly developed Hangang score ( 7 ) at our center, and the Acute Physiology and Chronic Health Evaluation Score (APACHE) IV ( 8 ). 2.1. Statistical Analyses Baseline demographic characteristics were reported as follows. Continuous, normal variables are presented as mean ± standard deviation (SD) values and non-normal variables are presented as medians (25th interquartile range [IQR] − 75th IQR). The independent-test or Wilcoxon signed rank test, depending on data normality was used to determine differences between the two groups. Categorical variables were analyzed using the Chi-square test and are presented as percentages. We used two methods of time-dependent receiver operating characteristic (ROC) curves to evaluate the time-varying prognostic performance using fixed baseline biomarkers measured at admission and updated biomarkers measured routinely during the study period. We calculated the incident/dynamic ROC using non-parametric rank-based approach, allocating subjects with an event at time into positive group and subject who experience event thereafter into negative group.( 9 ). The cumulative/dynamic ROC curve is performed by allocating subject who experienced the event before fixed point of time into positive group and eventless subject during time into negative group.( 10 ) The data for cumulative/dynamic ROC was subsetted to analyze the diagnostic performance at an every week from 1 week and 8th week. Confidence intervals were calculated using 500-time bootstrap resampling, and percentile-based confidence intervals were obtained. Two side p-value < 0.05 was considered statistically significant. All analyses were conducted by using computing statistical R-project program version 3.6. Results 3.1. Baseline Characteristics of the Survivors and Non-survivors. Total 2259 patients were included in this retrospective study; 1786 patients were allocated to the survival group and 473 to the non-survival group; the overall mortality was 20.9%. The overall median age was 48.0 y and was higher in the non-survival group than in the survival group (52.0 vs. 46.0). The overall median burnt TBSA was 24.0%; the value was higher at 65.0% in the non-survival group. The inhalation was significantly higher at 78.2% in the non-survival group. All the severity scores were significantly higher (62.0 in APAHCHE IV, 161.0 in Hangang, 12.0 in ABSI) in the non-survival group. All baseline laboratory results included in this study were collected at admission and were significantly different between two groups (Table 1 ). The overall numbers of measurement for each biomarker were 43455 in platelet, WBC, 43441 in creatinine, 43438 in TB, 43182 in lactate and 43340 in PT. Table 1 Baseline characteristics between the two groups Variables Survivors(n = 1786) Non-survivors(n = 473) Total(n = 2259) p age 46.0 [37.0;55.0] 52.0 [43.0;65.0] 48.0 [38.0;56.5] 0.000 Gender 0.495 - Male 1469 (82.3%) 382 (80.8%) 1851 (81.9%) TBSA 24.0 [14.0;37.0] 65.0 [42.0;85.0] 29.0 [17.0;48.0] 0.000 Type 0.000 - FB 1228 (68.8%) 421 (89.0%) 1649 (73.0%) - EB 318 (17.8%) 9 ( 1.9%) 327 (14.5%) - SB 156 ( 8.7%) 30 ( 6.3%) 186 ( 8.2%) - CoB 50 ( 2.8%) 10 ( 2.1%) 60 ( 2.7%) - ChB 34 ( 1.9%) 3 ( 0.6%) 37 ( 1.6%) Inhalation 812 (45.5%) 370 (78.2%) 1182 (52.3%) 0.000 APACHE_IV 33.0 [24.0;45.0] 62.0 [49.0;76.0] 38.0 [26.0;53.0] 0.000 Hangang 122.0 [113.0;132.0] 161.0 [149.0;177.0] 127.0 [115.0;144.0] 0.000 ABSI 7.0 [ 6.0; 9.0] 12.0 [10.0;14.0] 8.0 [ 6.0;10.0] 0.000 LOS 15.0 [ 6.0;35.0] 12.0 [ 7.0;22.0] 14.0 [ 6.0;32.0] 0.001 WBC 17.3 [13.0;22.7] 29.2 [20.6;37.4] 18.7 [13.7;26.0] 0.000 Platelet -230.5 [-280.0;-186.0] -193.0 [-269.0;-134.5] -225.0 [-279.0;-175.0] 0.000 Creatinie 0.8 [ 0.6; 0.9] 1.0 [ 0.8; 1.4] 0.8 [ 0.7; 1.0] 0.000 Lactate 2.6 [ 1.7; 4.0] 5.6 [ 3.9; 7.9] 3.0 [ 1.9; 5.0] 0.000 TB 0.8 [ 0.5; 1.1] 1.1 [ 0.8; 1.7] 0.8 [ 0.6; 1.2] 0.000 PT 11.8 [10.9;12.9] 13.1 [11.8;14.9] 12.0 [11.0;13.3] 0.000 n, number; FB, Flame Burn; SB, Scald Burn; EB, Electrical Burn; ChB, Chemical Burn; CoB, Contact Burn; %TBSA burned, percentage of total body surface area burned; APACHE, Acute Physiology and Chronic Health Evaluation Score; ABSI, Abbreviated Burn Severity Index; LOS, length of hospital stay; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell 3.2. Diagnostic Performance of Baseline and Updated Biomarkers over time using the ID Approach. Incident/dynamic (ID) ROC curve are particularly well suited for assessing the performance of markers measured at a series of time points during decision-making ( 11 ). First, look over AUC using baseline biomarker (Table 2 ), C-index which shows overall performance of different biomarkers were the highest in lactate with 0.662 (95% confidence interval [CI], 0.614 ~ 0.673), but, the value of AUC in lactate were from 0.786 (0.760 ~ 0.812) in 1st week to 0.574 (0.509 ~ 0.639) in 8th week showing decreasing trend. Platelet which had lower c-index with 0.576 (0.546 ~ 0.605) were from 0.576 (0.535 ~ 0.617) in 1st week to 0.711 (0.643 ~ 0.779) in 8th week showing increasing trend. For updated biomarkers (Table 3 ), platelet show the highest c-index with 0.930(0.919 ~ 0.941) as well as the highest during all time points. PT and creatinine show over 8 c-index with 0.862 (0.843 ~ 0.881) and 0.828 (0.809 ~ 0.848), respectively. (Fig. 1 ) Table 2 Time varying Performance of baseline biomarkers using ID approach (AUC with 95% CI) week1 week2 week3 week4 week5 week6 week7 week8 c-index Platelet 0.576 (0.535 ~ 0.617) 0.560 (0.516 ~ 0.604) 0.574 (0.52 ~ 0.628) 0.597 (0.534 ~ 0.659) 0.566 (0.483 ~ 0.649) 0.616 (0.527 ~ 0.705) 0.669 (0.595 ~ 0.742) 0.711 (0.643 ~ 0.779) 0.576 (0.546 ~ 0.605) Lactate 0.786 (0.760 ~ 0.812) 0.722 (0.688 ~ 0.755) 0.654 (0.605 ~ 0.703) 0.606 (0.545 ~ 0.667) 0.586 (0.504 ~ 0.668) 0.539 (0.458 ~ 0.619) 0.555 (0.484 ~ 0.625) 0.574 (0.509 ~ 0.639) 0.662 (0.633 ~ 0.69) WBC 0.713 (0.674 ~ 0.752) 0.701 (0.665 ~ 0.737) 0.683 (0.636 ~ 0.729) 0.630 (0.556 ~ 0.703) 0.615 (0.53 ~ 0.7) 0.575 (0.488 ~ 0.662) 0.489 (0.414 ~ 0.564) 0.522 (0.453 ~ 0.591) 0.644 (0.614 ~ 0.673) TB 0.614 (0.577 ~ 0.65) 0.589 (0.545 ~ 0.633) 0.596 (0.543 ~ 0.649) 0.577 (0.516 ~ 0.638) 0.506 (0.42 ~ 0.592) 0.467 (0.373 ~ 0.56) 0.414 (0.333 ~ 0.495) 0.448 (0.384 ~ 0.511) 0.572 (0.544 ~ 0.599) PT 0.681 (0.647 ~ 0.715) 0.623 (0.585 ~ 0.66) 0.616 (0.565 ~ 0.667) 0.635 (0.579 ~ 0.691) 0.633 (0.571 ~ 0.694) 0.644 (0.573 ~ 0.715) 0.648 (0.578 ~ 0.718) 0.643 (0.584 ~ 0.702) 0.634 (0.607 ~ 0.661) Creatinie 0.690 (0.655 ~ 0.725) 0.640 (0.599 ~ 0.68) 0.594 (0.539 ~ 0.649) 0.5645 (0.509 ~ 0.62) 0.561 (0.495 ~ 0.626) 0.558 (0.489 ~ 0.627) 0.506 (0.438 ~ 0.573) 0.518 (0.452 ~ 0.584) 0.615 (0.588 ~ 0.642) CI, confidence interval; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell Table 3 Time varying Performance of updated biomarker using ID approach (AUC with 95% CI) week1 week2 week3 week4 week5 week6 week7 week8 c-index Platelet 0.938 (0.922 ~ 0.954) 0.956 (0.946 ~ 0.966) 0.958 (0.941 ~ 0.975) 0.970 (0.956 ~ 0.984) 0.974 (0.96 ~ 0.988) 0.980 (0.968 ~ 0.992) 0.980 (0.968 ~ 0.992) 0.987 (0.973 ~ 1) 0.930 (0.919 ~ 0.941) Lactate 0.801 (0.771 ~ 0.83) 0.775 (0.739 ~ 0.81) 0.810 (0.774 ~ 0.846) 0.853 (0.809 ~ 0.897) 0.868 (0.82 ~ 0.916) 0.901 (0.854 ~ 0.948) 0.909 (0.858 ~ 0.96) 0.922 (0.873 ~ 0.97) 0.786 (0.758 ~ 0.814) WBC 0.653 (0.616 ~ 0.69) 0.707 (0.675 ~ 0.739) 0.753 (0.709 ~ 0.796) 0.813 (0.763 ~ 0.862) 0.817 (0.748 ~ 0.886) 0.770 (0.691 ~ 0.849) 0.714 (0.618 ~ 0.809) 0.703 (0.609 ~ 0.797) 0.684 (0.658 ~ 0.711) TB 0.745 (0.713 ~ 0.777) 0.820 (0.795 ~ 0.845) 0.910 (0.888 ~ 0.932) 0.937 (0.912 ~ 0.962) 0.900 (0.849 ~ 0.950) 0.897 (0.841 ~ 0.952) 0.910 (0.858 ~ 0.961) 0.946 (0.908 ~ 0.983) 0.782 (0.757 ~ 0.807) PT 0.872 (0.850 ~ 0.894) 0.871 (0.848 ~ 0.894) 0.905 (0.881 ~ 0.929) 0.933 (0.908 ~ 0.957) 0.900 (0.846 ~ 0.953) 0.897 (0.834 ~ 0.96) 0.898 (0.835 ~ 0.961) 0.950 (0.917 ~ 0.982) 0.862 (0.843 ~ 0.881) Creatinie 0.873 (0.850 ~ 0.895) 0.876 (0.854 ~ 0.897) 0.838 (0.805 ~ 0.871) 0.813 (0.761 ~ 0.864) 0.733 (0.653 ~ 0.813) 0.726 (0.634 ~ 0.818) 0.666 (0.568 ~ 0.764) 0.715 (0.626 ~ 0.803) 0.828 (0.809 ~ 0.848) CI, confidence interval; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell 3.3. Diagnostic Performance of Baseline and Updated Biomarkers over time by CD Approach. Cumulative/dynamic (CD) ROC curves are suitable tool for assessing prognostic accuracy when interested in identifying individuals at risk of event before time of interest.( 11 ) We set time of interest as 1 week. First, look over AUC using baseline biomarker, (Table 4 ) the value of AUC in platelet were from 0.562 (0.504 ~ 0.62) in 1st week to 0.888 (0.829 ~ 0.946) in 8th week showing increasing trend. Lactate were from 0.756 (0.714 ~ 0.798) in 1st week to 0.550 (0.356 ~ 0.743) in 8th week showing decreasing trend. For update biomarkers (Table 5 ), platelet show the highest value of AUC 0.871 (0.841 ~ 0.900) in 1st week and 0.999 (0.997 ~ 1.000) in 8th week. Lactate show the highest value of AUC with 0.999 (0.998 ~ 1.000) in 7th week, PT show the value of over 7 except week 6 (0.566, 95%CI 245 ~ 0.887) (Fig. 2 ). Table 4 Time varying Performance of baseline biomarkers using CD approach (AUC with 95% CI) week1 week2 week3 week4 week5 week6 week7 week8 Platelet 0.562 (0.504 ~ 0.620) 0.570 (0.496 ~ 0.643) 0.611 (0.531 ~ 0.691) 0.622 (0.507 ~ 0.737) 0.514 (0.351 ~ 0.677) 0.605 (0.401 ~ 0.808) 0.857 (0.793 ~ 0.920) 0.888 (0.829 ~ 0.946) Lactate 0.756 (0.714 ~ 0.798) 0.710 (0.649 ~ 0.77) 0.623 (0.555 ~ 0.691) 0.600 (0.486 ~ 0.713) 0.592 (0.412 ~ 0.772) 0.469 (0.289 ~ 0.649) 0.409 (0.223 ~ 0.594) 0.550 (0.356 ~ 0.743) WBC 0.689 (0.632 ~ 0.745) 0.679 (0.608 ~ 0.749) 0.612 (0.519 ~ 0.705) 0.618 (0.506 ~ 0.73) 0.603 (0.435 ~ 0.77) 0.511 (0.29 ~ 0.731) 0.427 (0.284 ~ 0.570) 0.263 (0.073 ~ 0.452) TB 0.617 (0.567 ~ 0.667) 0.599 (0.523 ~ 0.674) 0.654 (0.572 ~ 0.736) 0.612 (0.513 ~ 0.711) 0.421 (0.23 ~ 0.612) 0.483 (0.285 ~ 0.68) 0.512 (0.375 ~ 0.649) 0.489 (0.303 ~ 0.674) PT 0.635 (0.583 ~ 0.687) 0.596 (0.532 ~ 0.660) 0.596 (0.515 ~ 0.676) 0.659 (0.569 ~ 0.748) 0.637 (0.484 ~ 0.789) 0.564 (0.406 ~ 0.722) 0.672 (0.577 ~ 0.767) 0.674 (0.533 ~ 0.814) Creatinie 0.648 (0.597 ~ 0.699) 0.633 (0.567 ~ 0.699) 0.573 (0.493 ~ 0.653) 0.574 (0.480 ~ 0.668) 0.587 (0.428 ~ 0.745) 0.540 (0.315 ~ 0.764) 0.601 (0.443 ~ 0.758) 0.276 (0.215 ~ 0.336) TB, total bilirubin; PT, prothrombin time; WBC, white blood cell Table 5 Time varying Performance of updated biomarker using CD approach (AUC with 95% CI) week1 week2 week3 week4 week5 week6 week7 week8 Platelet 0.871 (0.841 ~ 0.900) 0.923 (0.899 ~ 0.946) 0.944 (0.922 ~ 0.966) 0.788 (0.694 ~ 0.881) 0.989 (0.979 ~ 0.999) 0.655 (0.445 ~ 0.864) 0.953 (0.913 ~ 0.992) 0.999 (0.997 ~ 1.000) Lactate 0.699 (0.657 ~ 0.741) 0.756 (0.692 ~ 0.819) 0.867 (0.83 ~ 0.904) 0.729 (0.648 ~ 0.81) 0.991 (0.983 ~ 0.999) 0.787 (0.669 ~ 0.904) 0.999 (0.998 ~ 1.000) 0.938 (0.887 ~ 0.989) WBC 0.572 (0.517 ~ 0.627) 0.615 (0.540 ~ 0.689) 0.679 (0.585 ~ 0.773) 0.695 (0.589 ~ 0.800) 0.994 (0.989 ~ 0.999) 0.897 (0.818 ~ 0.975) 0.532 (0.228 ~ 0.836) 0.659 (0.391 ~ 0.927) TB 0.595 (0.543 ~ 0.647) 0.686 (0.628 ~ 0.743) 0.822 (0.768 ~ 0.875) 0.746 (0.664 ~ 0.827) 0.982 (0.971 ~ 0.992) 0.438 (0.113 ~ 0.763) 0.935 (0.893 ~ 0.977) 0.960 (0.926 ~ 0.994) PT 0.737 (0.693 ~ 0.781) 0.719 (0.654 ~ 0.783) 0.775 (0.719 ~ 0.83) 0.730 (0.615 ~ 0.844) 0.983 (0.973 ~ 0.992) 0.566 (0.245 ~ 0.887) 0.975 (0.952 ~ 0.998) 0.841 (0.761 ~ 0.921) Creatinie 0.850 (0.815 ~ 0.885) 0.811 (0.736 ~ 0.885) 0.850 (0.792 ~ 0.908) 0.599 (0.498 ~ 0.699) 0.861 (0.827 ~ 0.895) 0.288 (0.201 ~ 0.375) 0.652 (0.337 ~ 0.967) 0.525 (0.367 ~ 0.683) TB, total bilirubin; PT, prothrombin time; WBC, white blood cell Discussions We evaluated time-varying diagnostic performance of biomarkers measured at admission only and updated the biomarkers at several time points in routine clinical setting. The performance of updated biomarkers by ID approach was higher than the baseline biomarkers in all situations with the exception of 1st (0.713 vs. 0.653) week in WBC (Table 1 ,2). The performance of updated biomarkers by CD approach was higher than the baseline biomarker in most parameters except lactate in 1st week (0.756 vs. 0.699), 1st (0.689 vs. 0.572), 2nd (0.679 vs. 0.615) week in WBC, 1st (0.617 vs. 0.595), 6th (0.483 vs. 0.438) in TB, 6th (0.540 vs. 0.288) in creatinine (Table 4 , 5 ). From the results, we identified that patient biomarkers must be regularly updated to maintain prognostic accuracy because good prognostic markers effectively suggest the choice and timing of therapeutic interventions, allowing timely action for individuals with the greatest risk of complications. The updated platelet had the highest c-index of 0.930 (95% CI, 0.919–0.941), which keeps the AUC I/D over 0.930 over time, indicating a strong prognostic biomarker for practical use. Moreover, we used AUC C/D over a period of 1 wk to actually evaluate the use of updated biomarkers as a decision tool. We found that AUC C/D of platelet was consistently higher than 0.870 at all the selected time points except at week 4 and 6. This indicates that platelet identifies high-risk patients at high-risk for mortality. Cate et al. ( 12 ) reported that platelet count is a strong predictor of mortality and showed AUC (0.779, 95% CI 0.697–0.862) that were calculated by the value measured on the 3rd day after admission. Huang et al. ( 13 ) reported platelets well discriminated mortality and showed AUC 0.782. Lactate has been used as a predictor by checking cellular hypoxia and shock and was reported that showed high prognostic performance of AUC with 0.82 ( 14 ). Adding lactate to severity scores predicts mortality better in critical ill patients. ( 15 ) In our study, lactate showed a relatively lower c-index with 0.786 than platelets, PT, and creatinine. We infer that this could be because lactate further reflects the severity of the burn than mortality. Creatinine is also a better risk factor of acute kidney injury (AKI) rather than mortality ( 16 ). However, creatinine showed high discrimination with c-index of 0.828. This is probably because AKI is one of the most common complications in burn patients. PT showed high c-index of 0.862 because PT was reported as a predictor in many diseases, such as liver disease, cardiac disease, and trauma ( 17 – 19 ). PT is reported to be an early predictor due to hepatic dysfunctions ( 20 ). However, PT was a good predictor throughout the period. There are certain limitations of this study. First, it was not multicenter study; thus, our population does not represent the entire population of Korea. However, our center is the only unit run by the University in Korea. Second, we set an arbitrary window period of 1 wk for CD approach to compare the biomarkers and thus cannot conclude how often the biomarkers should be updated. Conclusions To predict effectively, biomarkers should be updated regularly. Platelet count showed the best prognostic performance. PT and creatinine as prognostic factors for certain disease, and the diagnostic power is good at s specific time point, however, the overall diagnostic performance is good in burn patients. Lactate is known strong predictor, but showed relatively low prognostic performance in burn patients. List Of Abbreviations WBC, white blood cell; TB, total bilirubin; PT, prothrombin time; ROC, receiver operating characteristic; AUC, area under the curve; ID, incident/dynamic; ICU, intensive burn unit; AKI, acute kidney injury; BICU, burn intensive care unit; TBSA, total body surface area; ABSI, abbreviated burn severity index; APACHE, acute physiology and chronic health evaluation score; SD, standard deviation; IQR, interquartile range; n, number; FB, Flame Burn; SB, Scald Burn; EB, Electrical Burn; ChB, Chemical Burn; CoB, Contact Burn; CD, cumulative/dynamic Declarations Consent for publications Not applicable in this section. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was approved by the Institutional review board of the Hangang Sacred Heart Hospital, and informed consent was waived, as this study was retrospective in nature and did not include any intervention. Author Contributions Conceptualization, Dohern Kym and Jun Hur; Data curation, Jaechul Yoon; Formal analysis, Yong Suk Cho; Funding acquisition, Jun Hur; Investigation, Jun Hur; Methodology, Dohern Kym; Resources, Jae Hee Won; Software, Wook Chun; Validation, Jun Hur and Haejun Yim; Writing – original draft, Jun Hur; Writing – review & editing, Dohern Kym. All authors have read and agreed to the published version of the manuscript Funding This research was funded by a grant of the GenNBio, Republic of Korea, grant number (H20190944). Conflicts of Interest The authors declare no conflict of interest Acknowledgements Not applicable in this section. References Han Y, Albert PS, Berg CD, Wentzensen N, Katki HA, Liu D. Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies. arXiv preprint arXiv:190808093. 2019. Bansal A, Heagerty PJ. A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making. Medical decision making : an international journal of the Society for Medical Decision Making. 2018;38(8):904-16. Zhi L, Hu X, Xu J, Yu C, Shao H, Pan X, et al. The characteristics and correlation between the ischemia-reperfusion and changes of redox status in the early stage of severe burns. The American journal of emergency medicine. 2015;33(3):338-43. Wachtel TL, Berry CC, Wachtel EE, Frank HA. The inter-rater reliability of estimating the size of burns from various burn area chart drawings. Burns. 2000;26(2):156-70. Walker PF, Buehner MF, Wood LA, Boyer NL, Driscoll IR, Lundy JB, et al. Diagnosis and management of inhalation injury: an updated review. Crit Care. 2015;19:351. Tobiasen J, Hiebert JM, Edlich RF. The abbreviated burn severity index. Ann Emerg Med. 1982;11(5):260-2. Kim Y, Kym D, Hur J, Jeon J, Yoon J, Yim H, et al. Development of a risk prediction model (Hangang) and comparison with clinical severity scores in burn patients. PLoS One. 2019;14(2):e0211075. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med. 2006;34(5):1297-310. Díaz-Coto S, Martínez-Camblor P, Pérez-Fernández S. smoothROCtime: an R package for time-dependent ROC curve estimation. Computational Statistics. 2020:1-21. Zheng Y, Heagerty PJ. Prospective accuracy for longitudinal markers. Biometrics. 2007;63(2):332-41. Bansal A, Heagerty PJ. A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes. Diagnostic and Prognostic Research. 2019;3(1):14. Cato LD, Wearn CM, Bishop JRB, Stone MJ, Harrison P, Moiemen N. Platelet count: A predictor of sepsis and mortality in severe burns. Burns. 2018;44(2):288-97. Huang X, Guo F, Zhou Z, Chang M, Wang F, Dou Y, et al. Relation between dynamic changes of platelet counts and 30-day mortality in severely burned patients. Platelets. 2019;30(2):158-63. Mokline A, Abdenneji A, Rahmani I, Gharsallah L, Tlaili S, Harzallah I, et al. Lactate: prognostic biomarker in severely burned patients. Annals of burns and fire disasters. 2017;30(1):35. Aksu A, Gulen M, Avci A, Satar S. Adding lactate to SOFA and qSOFA scores predicts in-hospital mortality better in older patients in critical care. European Geriatric Medicine. 2019;10(3):445-53. Kim Y, Cho YS, Kym D, Yoon J, Yim H, Hur J, et al. Diagnostic performance of plasma and urine neutrophil gelatinase-associated lipocalin, cystatin C, and creatinine for acute kidney injury in burn patients: A prospective cohort study. PLoS One. 2018;13(6):e0199600. Karaca M, Bayata MS, Nazlı C. Prognostic Value of Prothrombin Time in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. 2018. Umebachi R, Taira T, Wakai S, Aoki H, Otsuka H, Nakagawa Y, et al. Measurement of blood lactate, D-dimer, and activated prothrombin time improves prediction of in-hospital mortality in adults blunt trauma. The American journal of emergency medicine. 2018;36(3):370-5. Gao F, Cai M-X, Lin M-T, Xie W, Zhang L-Z, Ruan Q-Z, et al. Prognostic value of international normalized ratio to albumin ratio among critically ill patients with cirrhosis. European journal of gastroenterology & hepatology. 2019;31(7):824-31. Saleh SM. The effect of moderate and severe burn injuries on human liver, kidney & blood (Biochemical study). Zagazig Journal of Forensic Medicine. 2018;16(1):91-102. Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2021 Read the published version in BMC Emergency Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-27589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original research","associatedPublications":[],"authors":[{"id":557333,"identity":"a58593b4-6131-418f-8c6f-28cc4dabbe28","order_by":1,"name":"Jaechul Yoon","email":"","orcid":"","institution":"Kangwon National University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jaechul","middleName":"","lastName":"Yoon","suffix":""},{"id":557334,"identity":"37471688-3a49-46ab-85cf-b02eae8e2ad0","order_by":2,"name":"Dohern Kym","email":"","orcid":"","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dohern","middleName":"","lastName":"Kym","suffix":""},{"id":557335,"identity":"ecd0554c-3fa0-481f-ab49-7f29bc68b9de","order_by":3,"name":"Jun Hur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACAwjFJmfAwNx4AMohTouxAQNjA0laGBI3EK3FnP106oaPe/jSt7MfbDjMm8MgZ97AlvYBnxbLntxtN2c8Y8vd2ZMI1LKNwVjmANvhGXgddiB3222eA2y5Gw5AtCTOYGBvxu+X82+33f5zgC3d4PxDYrXcANrCcIAtweAG3Ba2wwS0vN12s+cAm+GGGw8bDs7dJmEswcyWTMBhudtu/DhwTN7gfPLBB2+32chJsLcZ49UCBcfAJBMPgwQDAzMxGhgYasAk4w/iVI+CUTAKRsEIAwBRwFFK2ApfCwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1402-2297","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Hur","suffix":""},{"id":557336,"identity":"d4f47f94-07e5-465a-ba25-28d981032ea4","order_by":4,"name":"Jae Hee Won","email":"","orcid":"","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jae","middleName":"Hee","lastName":"Won","suffix":""},{"id":557337,"identity":"2a68e6c2-995c-4d7e-b0dc-6707c3b3dc9a","order_by":5,"name":"Haejun Yim","email":"","orcid":"","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haejun","middleName":"","lastName":"Yim","suffix":""},{"id":557338,"identity":"a4ce6631-78fb-4b67-8353-9cf9678cbe4c","order_by":6,"name":"Yong Suk Cho","email":"","orcid":"","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"Suk","lastName":"Cho","suffix":""},{"id":557339,"identity":"afa9b4cb-8c12-4799-b15d-4d0a789270a7","order_by":7,"name":"Wook Chun","email":"","orcid":"","institution":"Hangang Sacred Heart Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wook","middleName":"","lastName":"Chun","suffix":""}],"badges":[],"createdAt":"2020-05-08 07:51:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-27589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-27589/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12873-020-00394-z","type":"published","date":"2021-01-06T02:36:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1101019,"identity":"af4d258e-0611-4bb8-916a-a388750a09b8","added_by":"auto","created_at":"2020-05-14 21:04:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":321150,"visible":true,"origin":"","legend":"Diagnostic Performance of Baseline and Updated Biomarkers over time using the ID Approach. Error bar is 95%d confidence interval calculated by bootstrap.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-27589/v1/Fig1.jpg"},{"id":1101020,"identity":"d406a63e-5fde-4040-9728-d2b78a806113","added_by":"auto","created_at":"2020-05-14 21:04:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":388065,"visible":true,"origin":"","legend":"Diagnostic Performance of Baseline and Updated Biomarkers over time by CD Approach. Error bar is 95%d confidence interval calculated by bootstrap","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-27589/v1/Fig2.jpg"},{"id":13503553,"identity":"aa317f05-3db0-4312-a5b0-7b597362600b","added_by":"auto","created_at":"2021-09-16 23:19:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":536172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-27589/v1/e767f087-9085-4f39-b278-5ca23ccf72ec.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eTime-Varying Discrimination Accuracy of Longitudinal Biomarkers for the Prediction of Mortality Compared to Assessment at Fixed Time Point in Severe Burns Patients\u003c/p\u003e","fulltext":[{"header":"Backgroud","content":"\u003cp\u003eBurns represent one of most devastating traumas and results in high morbidity and mortality. Thus, the prediction of adverse effects of interest or mortality using updated biomarkers that are measured routinely over time is an essential part of care in an intensive care unit (ICU). The distribution of biomarker over time is considered an indicator of disease formation and is helpful in the early detection of disease, thus reducing the disease-related mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Many biomarkers that are measured at a single time, such as at admission or an event, such as the development of acute kidney injury (AKI) or intervention, have been used to predict the outcome at multiple time points of interest.\u003c/p\u003e \u003cp\u003eIf accurate predictions are possible, they could provide clinical recommendation about the selection and timing of interventions and help in the initiation of specific preventive strategies and aggressive treatment for high-risk individuals. Further, it could reduce the cost, adverse effects, and unnecessary interventions in low-risk patients. Ultimately, the goal of the prognostic model with the use of biomarkers is to accurately predict the time of the event or to distinguish cases and controls in every situation. In addition, the disease state of an individual changes over time; therefore, prognostic information, such as updated biomarkers recorded during routine measurements, also change and can affect the performance of decision-making tools.\u003c/p\u003e \u003cp\u003eThe concept of accuracy of sensitivity and specificity is fundamental to clinical research and decision modeling. Recently, statistical methods have been developed to generalize these traditional cross-sectional accuracy concepts for the application to time-varying characteristics of disease states.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) In clinical practice, the ability biomarkers to predict the outcome have been evaluated using a conventional, cross-sectional method. Therefore, we investigated the prognostic potential of biomarkers routinely used in clinical practice over time and compared whether different biomarkers have varying prognostic accuracy at different times during treatment.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eFrom February 2007 to December 2018, patients aged\u0026thinsp;\u0026gt;\u0026thinsp;18 y who were admitted within 24\u0026nbsp;h of the burn incident at the burn intensive care unit (BICU) of Hangang Sacred Heart Hospital, Hallym University Medical Center were included in this retrospective study; all the patients underwent acute fluid resuscitation during the first 3\u0026nbsp;days after the burn. The indications of admission to BICU were as follows: 1) partial thickness burn of \u0026gt;\u0026thinsp;20% of total body surface area (TBSA) for adults and partial thickness burn of \u0026gt;\u0026thinsp;10% of the TBSA in patients aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years, 2) inhalation injury, 3) electrical burn, 4) preexisting medical disorder that could incur complications or affect mortality, and 5) concomitant trauma that could elevate the risk of morbidity or mortality. Clinical longitudinal data that were measured routinely and were known predictors, such as white blood cell (WBC), platelet count, lactate (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), creatinine, total bilirubin (TB), and prothrombin time (PT) were retrieved from a clinical database warehouse at the Hangang Sacred Heart Hospital. The study period was the stay in the BICU. When the biomarkers were measured several times each day, we recorded the poorest value recorded. We also noted demographic variables, such as age, sex, TBSA (calculated by surgeon using a modified Lund and Browder chart) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), type of burn, length of BICU stay, and presence of inhalation injury (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The primary outcome was the ICU mortality. The severity of injury was reported using the Abbreviated Burn Severity Index (ABSI) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), the newly developed Hangang score (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) at our center, and the Acute Physiology and Chronic Health Evaluation Score (APACHE) IV (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Statistical Analyses\u003c/h2\u003e \u003cp\u003eBaseline demographic characteristics were reported as follows. Continuous, normal variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) values and non-normal variables are presented as medians (25th interquartile range [IQR] \u0026minus;\u0026thinsp;75th IQR). The independent-test or Wilcoxon signed rank test, depending on data normality was used to determine differences between the two groups. Categorical variables were analyzed using the Chi-square test and are presented as percentages. We used two methods of time-dependent receiver operating characteristic (ROC) curves to evaluate the time-varying prognostic performance using fixed baseline biomarkers measured at admission and updated biomarkers measured routinely during the study period. We calculated the incident/dynamic ROC using non-parametric rank-based approach, allocating subjects with an event at time into positive group and subject who experience event thereafter into negative group.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The cumulative/dynamic ROC curve is performed by allocating subject who experienced the event before fixed point of time into positive group and eventless subject during time into negative group.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The data for cumulative/dynamic ROC was subsetted to analyze the diagnostic performance at an every week from 1 week and 8th week. Confidence intervals were calculated using 500-time bootstrap resampling, and percentile-based confidence intervals were obtained. Two side p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were conducted by using computing statistical R-project program version 3.6.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003ch2\u003e3.1. Baseline Characteristics of the Survivors and Non-survivors.\u003c/h2\u003e \u003cp\u003eTotal 2259 patients were included in this retrospective study; 1786 patients were allocated to the survival group and 473 to the non-survival group; the overall mortality was 20.9%. The overall median age was 48.0 y and was higher in the non-survival group than in the survival group (52.0 vs. 46.0). The overall median burnt TBSA was 24.0%; the value was higher at 65.0% in the non-survival group. The inhalation was significantly higher at 78.2% in the non-survival group. All the severity scores were significantly higher (62.0 in APAHCHE IV, 161.0 in Hangang, 12.0 in ABSI) in the non-survival group. All baseline laboratory results included in this study were collected at admission and were significantly different between two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall numbers of measurement for each biomarker were 43455 in platelet, WBC, 43441 in creatinine, 43438 in TB, 43182 in lactate and 43340 in PT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics between the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvivors(n\u0026thinsp;=\u0026thinsp;1786)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors(n\u0026thinsp;=\u0026thinsp;473)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;2259)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.0 [37.0;55.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.0 [43.0;65.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.0 [38.0;56.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1469 (82.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e382 (80.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1851 (81.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.0 [14.0;37.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.0 [42.0;85.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.0 [17.0;48.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- FB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1228 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1649 (73.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- EB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e318 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 ( 1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e327 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- SB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156 ( 8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 ( 6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 ( 8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- CoB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 ( 2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 ( 2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60 ( 2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- ChB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 ( 1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 ( 0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37 ( 1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInhalation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e812 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e370 (78.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1182 (52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE_IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.0 [24.0;45.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.0 [49.0;76.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.0 [26.0;53.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHangang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122.0 [113.0;132.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.0 [149.0;177.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.0 [115.0;144.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.0 [ 6.0; 9.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0 [10.0;14.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.0 [ 6.0;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.0 [ 6.0;35.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0 [ 7.0;22.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.0 [ 6.0;32.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.3 [13.0;22.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.2 [20.6;37.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.7 [13.7;26.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-230.5 [-280.0;-186.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-193.0 [-269.0;-134.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-225.0 [-279.0;-175.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 [ 0.6; 0.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0 [ 0.8; 1.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8 [ 0.7; 1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6 [ 1.7; 4.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6 [ 3.9; 7.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0 [ 1.9; 5.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8 [ 0.5; 1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1 [ 0.8; 1.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8 [ 0.6; 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.8 [10.9;12.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.1 [11.8;14.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0 [11.0;13.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en, number; FB, Flame Burn; SB, Scald Burn; EB, Electrical Burn; ChB, Chemical Burn; CoB, Contact Burn; %TBSA burned, percentage of total body surface area burned; APACHE, Acute Physiology and Chronic Health Evaluation Score; ABSI, Abbreviated Burn Severity Index; LOS, length of hospital stay; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ch2\u003e3.2. Diagnostic Performance of Baseline and Updated Biomarkers over time using the ID Approach.\u003c/h2\u003e \u003cp\u003eIncident/dynamic (ID) ROC curve are particularly well suited for assessing the performance of markers measured at a series of time points during decision-making (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). First, look over AUC using baseline biomarker (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), C-index which shows overall performance of different biomarkers were the highest in lactate with 0.662 (95% confidence interval [CI], 0.614\u0026thinsp;~\u0026thinsp;0.673), but, the value of AUC in lactate were from 0.786 (0.760\u0026thinsp;~\u0026thinsp;0.812) in 1st week to 0.574 (0.509\u0026thinsp;~\u0026thinsp;0.639) in 8th week showing decreasing trend. Platelet which had lower c-index with 0.576 (0.546\u0026thinsp;~\u0026thinsp;0.605) were from 0.576 (0.535\u0026thinsp;~\u0026thinsp;0.617) in 1st week to 0.711 (0.643\u0026thinsp;~\u0026thinsp;0.779) in 8th week showing increasing trend. For updated biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), platelet show the highest c-index with 0.930(0.919\u0026thinsp;~\u0026thinsp;0.941) as well as the highest during all time points. PT and creatinine show over 8 c-index with 0.862 (0.843\u0026thinsp;~\u0026thinsp;0.881) and 0.828 (0.809\u0026thinsp;~\u0026thinsp;0.848), respectively. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime varying Performance of baseline biomarkers using ID approach (AUC with 95% CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweek1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweek2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweek3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweek4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweek5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweek6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eweek7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eweek8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ec-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.576 (0.535\u0026thinsp;~\u0026thinsp;0.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.560 (0.516\u0026thinsp;~\u0026thinsp;0.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.574 (0.52\u0026thinsp;~\u0026thinsp;0.628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.597 (0.534\u0026thinsp;~\u0026thinsp;0.659)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.566 (0.483\u0026thinsp;~\u0026thinsp;0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.616 (0.527\u0026thinsp;~\u0026thinsp;0.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.669 (0.595\u0026thinsp;~\u0026thinsp;0.742)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.711 (0.643\u0026thinsp;~\u0026thinsp;0.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.576 (0.546\u0026thinsp;~\u0026thinsp;0.605)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.786 (0.760\u0026thinsp;~\u0026thinsp;0.812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.722 (0.688\u0026thinsp;~\u0026thinsp;0.755)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654 (0.605\u0026thinsp;~\u0026thinsp;0.703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606 (0.545\u0026thinsp;~\u0026thinsp;0.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.586 (0.504\u0026thinsp;~\u0026thinsp;0.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.539 (0.458\u0026thinsp;~\u0026thinsp;0.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.555 (0.484\u0026thinsp;~\u0026thinsp;0.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.574 (0.509\u0026thinsp;~\u0026thinsp;0.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.662 (0.633\u0026thinsp;~\u0026thinsp;0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.713 (0.674\u0026thinsp;~\u0026thinsp;0.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.701 (0.665\u0026thinsp;~\u0026thinsp;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683 (0.636\u0026thinsp;~\u0026thinsp;0.729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.630 (0.556\u0026thinsp;~\u0026thinsp;0.703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.615 (0.53\u0026thinsp;~\u0026thinsp;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.575 (0.488\u0026thinsp;~\u0026thinsp;0.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.489 (0.414\u0026thinsp;~\u0026thinsp;0.564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.522 (0.453\u0026thinsp;~\u0026thinsp;0.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.644 (0.614\u0026thinsp;~\u0026thinsp;0.673)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.614 (0.577\u0026thinsp;~\u0026thinsp;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.589 (0.545\u0026thinsp;~\u0026thinsp;0.633)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596 (0.543\u0026thinsp;~\u0026thinsp;0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.577 (0.516\u0026thinsp;~\u0026thinsp;0.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.506 (0.42\u0026thinsp;~\u0026thinsp;0.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.467 (0.373\u0026thinsp;~\u0026thinsp;0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.414 (0.333\u0026thinsp;~\u0026thinsp;0.495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.448 (0.384\u0026thinsp;~\u0026thinsp;0.511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.572 (0.544\u0026thinsp;~\u0026thinsp;0.599)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.681 (0.647\u0026thinsp;~\u0026thinsp;0.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.623 (0.585\u0026thinsp;~\u0026thinsp;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616 (0.565\u0026thinsp;~\u0026thinsp;0.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.635 (0.579\u0026thinsp;~\u0026thinsp;0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633 (0.571\u0026thinsp;~\u0026thinsp;0.694)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.644 (0.573\u0026thinsp;~\u0026thinsp;0.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.648 (0.578\u0026thinsp;~\u0026thinsp;0.718)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.643 (0.584\u0026thinsp;~\u0026thinsp;0.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.634 (0.607\u0026thinsp;~\u0026thinsp;0.661)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.690 (0.655\u0026thinsp;~\u0026thinsp;0.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.640 (0.599\u0026thinsp;~\u0026thinsp;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594 (0.539\u0026thinsp;~\u0026thinsp;0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5645 (0.509\u0026thinsp;~\u0026thinsp;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.561 (0.495\u0026thinsp;~\u0026thinsp;0.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.558 (0.489\u0026thinsp;~\u0026thinsp;0.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.506 (0.438\u0026thinsp;~\u0026thinsp;0.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.518 (0.452\u0026thinsp;~\u0026thinsp;0.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.615 (0.588\u0026thinsp;~\u0026thinsp;0.642)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCI, confidence interval; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime varying Performance of updated biomarker using ID approach (AUC with 95% CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweek1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweek2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweek3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweek4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweek5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweek6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eweek7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eweek8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ec-index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938 (0.922\u0026thinsp;~\u0026thinsp;0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956 (0.946\u0026thinsp;~\u0026thinsp;0.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.958 (0.941\u0026thinsp;~\u0026thinsp;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.970 (0.956\u0026thinsp;~\u0026thinsp;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.974 (0.96\u0026thinsp;~\u0026thinsp;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.980 (0.968\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.980 (0.968\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.987 (0.973\u0026thinsp;~\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.930 (0.919\u0026thinsp;~\u0026thinsp;0.941)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.801 (0.771\u0026thinsp;~\u0026thinsp;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.775 (0.739\u0026thinsp;~\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.810 (0.774\u0026thinsp;~\u0026thinsp;0.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.853 (0.809\u0026thinsp;~\u0026thinsp;0.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.868 (0.82\u0026thinsp;~\u0026thinsp;0.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.901 (0.854\u0026thinsp;~\u0026thinsp;0.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.909 (0.858\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.922 (0.873\u0026thinsp;~\u0026thinsp;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.786 (0.758\u0026thinsp;~\u0026thinsp;0.814)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.653 (0.616\u0026thinsp;~\u0026thinsp;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.707 (0.675\u0026thinsp;~\u0026thinsp;0.739)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.753 (0.709\u0026thinsp;~\u0026thinsp;0.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.813 (0.763\u0026thinsp;~\u0026thinsp;0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.817 (0.748\u0026thinsp;~\u0026thinsp;0.886)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.770 (0.691\u0026thinsp;~\u0026thinsp;0.849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.714 (0.618\u0026thinsp;~\u0026thinsp;0.809)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.703 (0.609\u0026thinsp;~\u0026thinsp;0.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.684 (0.658\u0026thinsp;~\u0026thinsp;0.711)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.745 (0.713\u0026thinsp;~\u0026thinsp;0.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.820 (0.795\u0026thinsp;~\u0026thinsp;0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.910 (0.888\u0026thinsp;~\u0026thinsp;0.932)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937 (0.912\u0026thinsp;~\u0026thinsp;0.962)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.900 (0.849\u0026thinsp;~\u0026thinsp;0.950)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.897 (0.841\u0026thinsp;~\u0026thinsp;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.910 (0.858\u0026thinsp;~\u0026thinsp;0.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.946 (0.908\u0026thinsp;~\u0026thinsp;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.782 (0.757\u0026thinsp;~\u0026thinsp;0.807)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872 (0.850\u0026thinsp;~\u0026thinsp;0.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871 (0.848\u0026thinsp;~\u0026thinsp;0.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905 (0.881\u0026thinsp;~\u0026thinsp;0.929)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933 (0.908\u0026thinsp;~\u0026thinsp;0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.900 (0.846\u0026thinsp;~\u0026thinsp;0.953)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.897 (0.834\u0026thinsp;~\u0026thinsp;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.898 (0.835\u0026thinsp;~\u0026thinsp;0.961)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.950 (0.917\u0026thinsp;~\u0026thinsp;0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.862 (0.843\u0026thinsp;~\u0026thinsp;0.881)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.873 (0.850\u0026thinsp;~\u0026thinsp;0.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876 (0.854\u0026thinsp;~\u0026thinsp;0.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.838 (0.805\u0026thinsp;~\u0026thinsp;0.871)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.813 (0.761\u0026thinsp;~\u0026thinsp;0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.733 (0.653\u0026thinsp;~\u0026thinsp;0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.726 (0.634\u0026thinsp;~\u0026thinsp;0.818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.666 (0.568\u0026thinsp;~\u0026thinsp;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.715 (0.626\u0026thinsp;~\u0026thinsp;0.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.828 (0.809\u0026thinsp;~\u0026thinsp;0.848)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eCI, confidence interval; TB, total bilirubin; PT, prothrombin time; WBC, white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003ch2\u003e3.3. Diagnostic Performance of Baseline and Updated Biomarkers over time by CD Approach.\u003c/h2\u003e \u003cp\u003eCumulative/dynamic (CD) ROC curves are suitable tool for assessing prognostic accuracy when interested in identifying individuals at risk of event before time of interest.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) We set time of interest as 1 week. First, look over AUC using baseline biomarker, (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) the value of AUC in platelet were from 0.562 (0.504\u0026thinsp;~\u0026thinsp;0.62) in 1st week to 0.888 (0.829\u0026thinsp;~\u0026thinsp;0.946) in 8th week showing increasing trend. Lactate were from 0.756 (0.714\u0026thinsp;~\u0026thinsp;0.798) in 1st week to 0.550 (0.356\u0026thinsp;~\u0026thinsp;0.743) in 8th week showing decreasing trend. For update biomarkers (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), platelet show the highest value of AUC 0.871 (0.841\u0026thinsp;~\u0026thinsp;0.900) in 1st week and 0.999 (0.997\u0026thinsp;~\u0026thinsp;1.000) in 8th week. Lactate show the highest value of AUC with 0.999 (0.998\u0026thinsp;~\u0026thinsp;1.000) in 7th week, PT show the value of over 7 except week 6 (0.566, 95%CI 245\u0026thinsp;~\u0026thinsp;0.887) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime varying Performance of baseline biomarkers using CD approach (AUC with 95% CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweek1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweek2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweek3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweek4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweek5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweek6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eweek7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eweek8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562 (0.504\u0026thinsp;~\u0026thinsp;0.620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.570 (0.496\u0026thinsp;~\u0026thinsp;0.643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.611 (0.531\u0026thinsp;~\u0026thinsp;0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.622 (0.507\u0026thinsp;~\u0026thinsp;0.737)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.514 (0.351\u0026thinsp;~\u0026thinsp;0.677)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.605 (0.401\u0026thinsp;~\u0026thinsp;0.808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.857 (0.793\u0026thinsp;~\u0026thinsp;0.920)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.888 (0.829\u0026thinsp;~\u0026thinsp;0.946)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.756 (0.714\u0026thinsp;~\u0026thinsp;0.798)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.710 (0.649\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623 (0.555\u0026thinsp;~\u0026thinsp;0.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.600 (0.486\u0026thinsp;~\u0026thinsp;0.713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.592 (0.412\u0026thinsp;~\u0026thinsp;0.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.469 (0.289\u0026thinsp;~\u0026thinsp;0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.409 (0.223\u0026thinsp;~\u0026thinsp;0.594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.550 (0.356\u0026thinsp;~\u0026thinsp;0.743)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.689 (0.632\u0026thinsp;~\u0026thinsp;0.745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.679 (0.608\u0026thinsp;~\u0026thinsp;0.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.612 (0.519\u0026thinsp;~\u0026thinsp;0.705)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.618 (0.506\u0026thinsp;~\u0026thinsp;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.603 (0.435\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.511 (0.29\u0026thinsp;~\u0026thinsp;0.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.427 (0.284\u0026thinsp;~\u0026thinsp;0.570)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.263 (0.073\u0026thinsp;~\u0026thinsp;0.452)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.617 (0.567\u0026thinsp;~\u0026thinsp;0.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599 (0.523\u0026thinsp;~\u0026thinsp;0.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654 (0.572\u0026thinsp;~\u0026thinsp;0.736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.612 (0.513\u0026thinsp;~\u0026thinsp;0.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.421 (0.23\u0026thinsp;~\u0026thinsp;0.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.483 (0.285\u0026thinsp;~\u0026thinsp;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.512 (0.375\u0026thinsp;~\u0026thinsp;0.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.489 (0.303\u0026thinsp;~\u0026thinsp;0.674)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.635 (0.583\u0026thinsp;~\u0026thinsp;0.687)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.596 (0.532\u0026thinsp;~\u0026thinsp;0.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596 (0.515\u0026thinsp;~\u0026thinsp;0.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659 (0.569\u0026thinsp;~\u0026thinsp;0.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.637 (0.484\u0026thinsp;~\u0026thinsp;0.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.564 (0.406\u0026thinsp;~\u0026thinsp;0.722)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.672 (0.577\u0026thinsp;~\u0026thinsp;0.767)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.674 (0.533\u0026thinsp;~\u0026thinsp;0.814)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648 (0.597\u0026thinsp;~\u0026thinsp;0.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.633 (0.567\u0026thinsp;~\u0026thinsp;0.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573 (0.493\u0026thinsp;~\u0026thinsp;0.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.574 (0.480\u0026thinsp;~\u0026thinsp;0.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.587 (0.428\u0026thinsp;~\u0026thinsp;0.745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.540 (0.315\u0026thinsp;~\u0026thinsp;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.601 (0.443\u0026thinsp;~\u0026thinsp;0.758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.276 (0.215\u0026thinsp;~\u0026thinsp;0.336)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eTB, total bilirubin; PT, prothrombin time; WBC, white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime varying Performance of updated biomarker using CD approach (AUC with 95% CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eweek1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweek2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eweek3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweek4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eweek5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eweek6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eweek7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eweek8\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871 (0.841\u0026thinsp;~\u0026thinsp;0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923 (0.899\u0026thinsp;~\u0026thinsp;0.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.944 (0.922\u0026thinsp;~\u0026thinsp;0.966)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.788 (0.694\u0026thinsp;~\u0026thinsp;0.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.989 (0.979\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.655 (0.445\u0026thinsp;~\u0026thinsp;0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.953 (0.913\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.999 (0.997\u0026thinsp;~\u0026thinsp;1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.699 (0.657\u0026thinsp;~\u0026thinsp;0.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756 (0.692\u0026thinsp;~\u0026thinsp;0.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.867 (0.83\u0026thinsp;~\u0026thinsp;0.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.729 (0.648\u0026thinsp;~\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.991 (0.983\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.787 (0.669\u0026thinsp;~\u0026thinsp;0.904)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.999 (0.998\u0026thinsp;~\u0026thinsp;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.938 (0.887\u0026thinsp;~\u0026thinsp;0.989)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.572 (0.517\u0026thinsp;~\u0026thinsp;0.627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615 (0.540\u0026thinsp;~\u0026thinsp;0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.679 (0.585\u0026thinsp;~\u0026thinsp;0.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.695 (0.589\u0026thinsp;~\u0026thinsp;0.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.994 (0.989\u0026thinsp;~\u0026thinsp;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.897 (0.818\u0026thinsp;~\u0026thinsp;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.532 (0.228\u0026thinsp;~\u0026thinsp;0.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.659 (0.391\u0026thinsp;~\u0026thinsp;0.927)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.595 (0.543\u0026thinsp;~\u0026thinsp;0.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.686 (0.628\u0026thinsp;~\u0026thinsp;0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.822 (0.768\u0026thinsp;~\u0026thinsp;0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746 (0.664\u0026thinsp;~\u0026thinsp;0.827)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982 (0.971\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.438 (0.113\u0026thinsp;~\u0026thinsp;0.763)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.935 (0.893\u0026thinsp;~\u0026thinsp;0.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.960 (0.926\u0026thinsp;~\u0026thinsp;0.994)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.737 (0.693\u0026thinsp;~\u0026thinsp;0.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.719 (0.654\u0026thinsp;~\u0026thinsp;0.783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.775 (0.719\u0026thinsp;~\u0026thinsp;0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.730 (0.615\u0026thinsp;~\u0026thinsp;0.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.983 (0.973\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.566 (0.245\u0026thinsp;~\u0026thinsp;0.887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.975 (0.952\u0026thinsp;~\u0026thinsp;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.841 (0.761\u0026thinsp;~\u0026thinsp;0.921)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.850 (0.815\u0026thinsp;~\u0026thinsp;0.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.811 (0.736\u0026thinsp;~\u0026thinsp;0.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850 (0.792\u0026thinsp;~\u0026thinsp;0.908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.599 (0.498\u0026thinsp;~\u0026thinsp;0.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.861 (0.827\u0026thinsp;~\u0026thinsp;0.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.288 (0.201\u0026thinsp;~\u0026thinsp;0.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.652 (0.337\u0026thinsp;~\u0026thinsp;0.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.525 (0.367\u0026thinsp;~\u0026thinsp;0.683)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eTB, total bilirubin; PT, prothrombin time; WBC, white blood cell\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Discussions","content":"\u003cp\u003eWe evaluated time-varying diagnostic performance of biomarkers measured at admission only and updated the biomarkers at several time points in routine clinical setting. The performance of updated biomarkers by ID approach was higher than the baseline biomarkers in all situations with the exception of 1st (0.713 vs. 0.653) week in WBC (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e ,2). The performance of updated biomarkers by CD approach was higher than the baseline biomarker in most parameters except lactate in 1st week (0.756 vs. 0.699), 1st (0.689 vs. 0.572), 2nd (0.679 vs. 0.615) week in WBC, 1st (0.617 vs. 0.595), 6th (0.483 vs. 0.438) in TB, 6th (0.540 vs. 0.288) in creatinine (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e,\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From the results, we identified that patient biomarkers must be regularly updated to maintain prognostic accuracy because good prognostic markers effectively suggest the choice and timing of therapeutic interventions, allowing timely action for individuals with the greatest risk of complications.\u003c/p\u003e \u003cp\u003eThe updated platelet had the highest c-index of 0.930 (95% CI, 0.919\u0026ndash;0.941), which keeps the AUC I/D over 0.930 over time, indicating a strong prognostic biomarker for practical use. Moreover, we used AUC C/D over a period of 1 wk to actually evaluate the use of updated biomarkers as a decision tool. We found that AUC C/D of platelet was consistently higher than 0.870\u0026nbsp;at all the selected time points except at week 4 and 6. This indicates that platelet identifies high-risk patients at high-risk for mortality. Cate et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) reported that platelet count is a strong predictor of mortality and showed AUC (0.779, 95% CI 0.697\u0026ndash;0.862) that were calculated by the value measured on the 3rd day after admission. Huang et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) reported platelets well discriminated mortality and showed AUC 0.782. Lactate has been used as a predictor by checking cellular hypoxia and shock and was reported that showed high prognostic performance of AUC with 0.82 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Adding lactate to severity scores predicts mortality better in critical ill patients. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) In our study, lactate showed a relatively lower c-index with 0.786 than platelets, PT, and creatinine. We infer that this could be because lactate further reflects the severity of the burn than mortality. Creatinine is also a better risk factor of acute kidney injury (AKI) rather than mortality (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, creatinine showed high discrimination with c-index of 0.828. This is probably because AKI is one of the most common complications in burn patients. PT showed high c-index of 0.862 because PT was reported as a predictor in many diseases, such as liver disease, cardiac disease, and trauma (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). PT is reported to be an early predictor due to hepatic dysfunctions (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, PT was a good predictor throughout the period.\u003c/p\u003e \u003cp\u003eThere are certain limitations of this study. First, it was not multicenter study; thus, our population does not represent the entire population of Korea. However, our center is the only unit run by the University in Korea. Second, we set an arbitrary window period of 1 wk for CD approach to compare the biomarkers and thus cannot conclude how often the biomarkers should be updated.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eTo predict effectively, biomarkers should be updated regularly. Platelet count showed the best prognostic performance. PT and creatinine as prognostic factors for certain disease, and the diagnostic power is good at s specific time point, however, the overall diagnostic performance is good in burn patients. Lactate is known strong predictor, but showed relatively low prognostic performance in burn patients.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eWBC, white blood cell; TB, total bilirubin; PT, prothrombin time; ROC, receiver operating characteristic; AUC, area under the curve; ID, incident/dynamic; ICU, intensive burn unit; AKI, acute kidney injury; BICU, burn intensive care unit; TBSA, total body surface area; ABSI, abbreviated burn severity index; APACHE, acute physiology and chronic health evaluation score; SD, standard deviation; IQR, interquartile range; n, number; FB, Flame Burn; SB, Scald Burn; EB, Electrical Burn; ChB, Chemical Burn; CoB, Contact Burn; CD, cumulative/dynamic\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publications\u003c/h2\u003e\u003cp\u003e\nNot applicable in this section.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis study was approved by the Institutional review board of the Hangang Sacred Heart Hospital, and informed consent was waived, as this study was retrospective in nature and did not include any intervention.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions \u003c/h2\u003e\u003cp\u003eConceptualization, Dohern Kym and Jun Hur; Data curation, Jaechul Yoon; Formal analysis, Yong Suk Cho; Funding acquisition, Jun Hur; Investigation, Jun Hur; Methodology, Dohern Kym; Resources, Jae Hee Won; Software, Wook Chun; Validation, Jun Hur and Haejun Yim; Writing – original draft, Jun Hur; Writing – review \u0026 editing, Dohern Kym.\u003cbr\u003e\nAll authors have read and agreed to the published version of the manuscript\u003c/p\u003e\n \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by a grant of the GenNBio, Republic of Korea, grant number (H20190944).\u003c/p\u003e \u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e \u003c/p\u003e \u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable in this section.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHan Y, Albert PS, Berg CD, Wentzensen N, Katki HA, Liu D. Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies. arXiv preprint arXiv:190808093. 2019.\u003c/li\u003e\n\u003cli\u003eBansal A, Heagerty PJ. A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making. Medical decision making : an international journal of the Society for Medical Decision Making. 2018;38(8):904-16.\u003c/li\u003e\n\u003cli\u003eZhi L, Hu X, Xu J, Yu C, Shao H, Pan X, et al. The characteristics and correlation between the ischemia-reperfusion and changes of redox status in the early stage of severe burns. The American journal of emergency medicine. 2015;33(3):338-43.\u003c/li\u003e\n\u003cli\u003eWachtel TL, Berry CC, Wachtel EE, Frank HA. The inter-rater reliability of estimating the size of burns from various burn area chart drawings. Burns. 2000;26(2):156-70.\u003c/li\u003e\n\u003cli\u003eWalker PF, Buehner MF, Wood LA, Boyer NL, Driscoll IR, Lundy JB, et al. Diagnosis and management of inhalation injury: an updated review. Crit Care. 2015;19:351.\u003c/li\u003e\n\u003cli\u003eTobiasen J, Hiebert JM, Edlich RF. The abbreviated burn severity index. Ann Emerg Med. 1982;11(5):260-2.\u003c/li\u003e\n\u003cli\u003eKim Y, Kym D, Hur J, Jeon J, Yoon J, Yim H, et al. Development of a risk prediction model (Hangang) and comparison with clinical severity scores in burn patients. PLoS One. 2019;14(2):e0211075.\u003c/li\u003e\n\u003cli\u003eZimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit Care Med. 2006;34(5):1297-310.\u003c/li\u003e\n\u003cli\u003eD\u0026iacute;az-Coto S, Mart\u0026iacute;nez-Camblor P, P\u0026eacute;rez-Fern\u0026aacute;ndez S. smoothROCtime: an R package for time-dependent ROC curve estimation. Computational Statistics. 2020:1-21.\u003c/li\u003e\n\u003cli\u003eZheng Y, Heagerty PJ. Prospective accuracy for longitudinal markers. Biometrics. 2007;63(2):332-41.\u003c/li\u003e\n\u003cli\u003eBansal A, Heagerty PJ. A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes. Diagnostic and Prognostic Research. 2019;3(1):14.\u003c/li\u003e\n\u003cli\u003eCato LD, Wearn CM, Bishop JRB, Stone MJ, Harrison P, Moiemen N. Platelet count: A predictor of sepsis and mortality in severe burns. Burns. 2018;44(2):288-97.\u003c/li\u003e\n\u003cli\u003eHuang X, Guo F, Zhou Z, Chang M, Wang F, Dou Y, et al. Relation between dynamic changes of platelet counts and 30-day mortality in severely burned patients. Platelets. 2019;30(2):158-63.\u003c/li\u003e\n\u003cli\u003eMokline A, Abdenneji A, Rahmani I, Gharsallah L, Tlaili S, Harzallah I, et al. Lactate: prognostic biomarker in severely burned patients. Annals of burns and fire disasters. 2017;30(1):35.\u003c/li\u003e\n\u003cli\u003eAksu A, Gulen M, Avci A, Satar S. Adding lactate to SOFA and qSOFA scores predicts in-hospital mortality better in older patients in critical care. European Geriatric Medicine. 2019;10(3):445-53.\u003c/li\u003e\n\u003cli\u003eKim Y, Cho YS, Kym D, Yoon J, Yim H, Hur J, et al. Diagnostic performance of plasma and urine neutrophil gelatinase-associated lipocalin, cystatin C, and creatinine for acute kidney injury in burn patients: A prospective cohort study. PLoS One. 2018;13(6):e0199600.\u003c/li\u003e\n\u003cli\u003eKaraca M, Bayata MS, Nazlı C. Prognostic Value of Prothrombin Time in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. 2018.\u003c/li\u003e\n\u003cli\u003eUmebachi R, Taira T, Wakai S, Aoki H, Otsuka H, Nakagawa Y, et al. Measurement of blood lactate, D-dimer, and activated prothrombin time improves prediction of in-hospital mortality in adults blunt trauma. The American journal of emergency medicine. 2018;36(3):370-5.\u003c/li\u003e\n\u003cli\u003eGao F, Cai M-X, Lin M-T, Xie W, Zhang L-Z, Ruan Q-Z, et al. Prognostic value of international normalized ratio to albumin ratio among critically ill patients with cirrhosis. European journal of gastroenterology \u0026amp; hepatology. 2019;31(7):824-31.\u003c/li\u003e\n\u003cli\u003eSaleh SM. The effect of moderate and severe burn injuries on human liver, kidney \u0026amp; blood (Biochemical study). Zagazig Journal of Forensic Medicine. 2018;16(1):91-102.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ROC, time-varying, discrimination, mortality, longitudinal, burns","lastPublishedDoi":"10.21203/rs.3.rs-27589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-27589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe distribution of biomarkers over time is considered an indicator of disease formation and helps in early detection of disease, thus reducing the disease-related mortality. The ability of biomarkers to predict outcomes has been evaluated using conventional cross-sectional methods; therefore, we investigated the prognostic potential of longitudinal biomarkers over time.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe patients aged \u0026gt; 18 y who were admitted within 24\u0026nbsp;h of the burn incident in the burn intensive care unit were enrolled. Longitudinal biomarkers, such as WBC, platelet count, lactate, creatinine, TB, and PT were retrieved from a clinical database warehouse. Time-dependent ROC curves using a cumulative/dynamic and incident/dynamic approach were employed to evaluate time-varying prognostic performance.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTotal 2259 patients were included in this retrospective study and divided into the survival group and non-survival group. With AUC using the ID approach, platelets showed the highest c-index with 0.930 (0.919 ~ 0.941) as well as the highest during all time points. PT and creatinine show over 8 c-index with 0.862 (0.843 ~ 0.881) and 0.828 (0.809 ~ 0.848), respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe platelet count was the best prognostic marker. PT and creatinine also showed good overall diagnostic ability. Lactate is known as a strong predictor; however, it showed relatively poorer prognostic performance in burns.\u003c/p\u003e","manuscriptTitle":"Time-Varying Discrimination Accuracy of Longitudinal Biomarkers for the Prediction of Mortality Compared to Assessment at Fixed Time Point in Severe Burns Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-05-14 21:03:31","doi":"10.21203/rs.3.rs-27589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c0744910-528c-48f1-84b8-5d04d5b93618","owner":[],"postedDate":"May 14th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":100222,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2021-07-22T02:36:48+00:00","versionOfRecord":{"articleIdentity":"rs-27589","link":"https://doi.org/10.1186/s12873-020-00394-z","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2021-01-06 02:36:48","publishedOnDateReadable":"January 6th, 2021"},"versionCreatedAt":"2020-05-14 21:03:31","video":"","vorDoi":"10.1186/s12873-020-00394-z","vorDoiUrl":"https://doi.org/10.1186/s12873-020-00394-z","workflowStages":[]},"version":"v1","identity":"rs-27589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-27589","identity":"rs-27589","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}