Prothrombin

In UPMC derivation and validation data, indicators were generated for each component of the systemic inflammatory response syndrome (SIRS) criteria^{4 (link)}; the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score^{8 (link)}; and the Logistic Organ Dysfunction System (LODS) score,^{9 (link)} a weighted organ dysfunction score (Table 1). We used a modified version of the LODS score that did not contain urine output (because of poor accuracy in recording on hospital ward encounters), prothrombin, or urea levels. The maximum SIRS criteria, SOFA score, and modified LODS score were calculated for the time window from 48 hours before to 24 hours after the onset of infection, as well as on each calendar day. This window was used for candidate criteria because organ dysfunction in sepsis may occur prior to, near the moment of, or after infection is recognized by clinicians or when a patient presents for care. Moreover, the clinical documentation, reporting of laboratory values in EHRs, and trajectory of organ dysfunction are heterogeneous across encounters and health systems. In a post hoc analysis requested by the task force, a change in SOFA score was calculated of 2 points or more from up to 48 hours before to up to 24 hours after the onset of infection.

This investigation was performed in the CPCCRN of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (8 (link)). Detailed methods for the TOPICC data collection have been previously described (6 (link)). There were seven sites, and one was composed of two institutions. In brief, patients from newborn to less than 18 years were randomly selected and stratified by hospital from December 4, 2011, to April 7, 2013. Patients from both general/medical and cardiac/cardiovascular PICUs were included. Moribund patients (vital signs incompatible with life for the first 2 hr after PICU admission) were excluded. Only the first PICU admission during hospitalization was included. The protocol was approved by all participating institutional review boards. Other analyses using this database have been published (6 (link), 7 (link), 9 (link), 10 (link)).
Data included descriptive and demographic information (Table 1). Interventions included both surgery and interventional catheterization. Cardiac arrest included closed chest massage within 24 hours before hospitalization or after hospital admission but before PICU admission. Admission source was classified as emergency department, inpatient unit, postintervention unit, or admission from another institution. Diagnosis was classified by the system of primary dysfunction based on the reason for PICU admission; cardiovascular conditions were classified as congenital or acquired.
The primary outcome in this analysis was hospital survival versus death.
Physiologic status was measured using the PRISM physiologic variables (5 (link)) with a shortened time interval (2 hr before PICU admission to 4 hr after admission for laboratory data and the first 4 hr of PICU care for other physiologic variables). For model building, the PRISM components were separated into cardiovascular (heart rate, systolic blood pressure, and temperature), neurologic (pupillary reactivity and mental status), respiratory (arterial P_o2, pH, P_co2, and total bicarbonate), chemical (glucose, potassium, blood urea nitrogen, and creatinine), and hematologic (WBC count, platelet count, prothrombin, and partial thromboplastin time) components, and the total PRISM was also separated into neurologic and non-neurologic categories.
The time interval for assessing PRISM data was modified for cardiac patients under 91 days old because some institutions admit infants to the PICU before a cardiac intervention to “optimize” the clinical status but not for intensive care; in these cases, the postintervention period more accurately reflects intensive care. However, in other infants for whom the cardiac intervention is delayed after PICU admission or the intervention is a therapy required because of failed medical management of the acute condition, the routine PRISM data collection time interval is an appropriate reflection of critical illness. Therefore, we identified infants for whom it would be more appropriate to use data from the 4 hours after the cardiac intervention (postintervention time interval) and those for whom using the admission time interval was more appropriate. We operationalized this decision on the conditions likely to present within the first 90 days, the time period when the vast majority of these conditions present (Table 2).
Statistical analyses used SAS 9.4 (SAS Institute Inc., Cary, NC) for descriptive statistics, model development, and fit assessment and R 3.1.1 (The R Foundation for Statistical Computing, Vienna, Austria; http://www.wu.ac.at/statmath) for evaluation of predictive ability. Patient characteristics were descriptively compared and evaluated across sites using the Kruskal-Wallis test for continuous variables and the Pearson chi-square test for categorical variables. The statistical analysis was under the direction of R.H.
The dataset was randomly divided into a derivation set (75%) for model building and a validation set (25%) stratified by the study site. Univariate mortality odds ratios were computed, and variables with a significance level of less than 0.1 were considered candidate predictors for the final model. As was the case for the previously published trichotomous (death, survival with significant new morbidity, and intact survival) model construction, a nonautomated (examined by biostatistician and clinician at each step) backward stepwise selection approach was used to select factors. Multicategorical factors (e.g., diagnostic categories) had factors combined when appropriate per statistical and clinical criteria. Clinician input was included (and paramount) in this process to ensure that the model fit was relevant and consistent with clinical information. Construction of a clinically relevant, sufficiently predictive model using predictors readily available to the clinician took precedence over inclusion based solely on statistical significance. We were cognizant of the existing trichotomous outcome model and attempted, when statistically justified, to create a compatible two-outcome model that could aid in a smooth transition to using the three-outcome approach.
Final candidate models were evaluated based on 2D receiver operating characteristic (ROC) curves (discrimination) and the Hosmer-Lemeshow goodness of fit (calibration). For the entire dataset, goodness of fit with respect to key subgroups was assessed by examining SMRs for descriptive and diagnostic categories not used in the final model. Only categories with at least 10 outcomes in observed and expected cells were used.

In 1987-89, the ARIC Study recruited to a baseline examination a cohort of 15,792 men and women aged 45-64 years, predominantly whites or African Americans, from four U.S. communities (12 (link)). Participants were re-examined in 1990-92 (93% response), 1993-95 (86%) and 1996-98 (80%). Participants in the ARIC Visit 4 examination serve as the cohort for the present analysis.
CRP was measured in 2008 on plasma frozen at −70°C from Visit 4 by the immunoturbidimetric assay using the Siemens (Dade Behring) BNII analyzer (Dade Behring, Deerfield, Ill), performed according to the manufacturer's protocol. Approximately 4% of samples were split and measured as blinded replicates on different dates to assess repeatability. The reliability coefficient for blinded quality control replicates of CRP was 0.99 (421 blinded replicates). Body mass index was assessed as weight (kg) in a scrub suit divided by height (m) squared. Statins were assessed by reviewing participants' medication containers. After Visit 4, cholesterol-lowering medications were self-reported during annual telephone contact. Factor VIII_c and aPTT were not measured at Visit 4 so the Visit 1 value (3 (link), 13 (link)) was used. Factor V Leiden and the prothrombin G20210A polymorphism were not measured in the whole ARIC cohort.
Participants were followed from Visit 4 (1996-98, n = 11,573) through 2005 to identify hospitalized VTE events. These were validated by physician review using a standardized protocol (14 (link)). A total of 263 VTE events were identified, of which only 7 had been included in our previous analysis of baseline CRP and VTE through June 1997 (3 (link)). Excluding these 7 events had no impact on this analysis, so we chose to include them.
Our hypothesis was that CRP would be associated positively with VTE incidence. From the 11,573 participants at Visit 4, we excluded 320 who were missing CRP; 331 with CRP values >20 mg/L, due to possible acute phase response; 342 who had a prior history of VTE; or 204 who were taking warfarin. This left 10,505 at risk: 8,219 whites, 2,255 African Americans, and 31 others, who were grouped with African Americans for this analysis. Follow-up time ended when the participant had a VTE, died, was lost to follow-up, or else until December 31, 2005. Cox proportional hazards regression was used to model the association between CRP and VTE incidence, and to derive hazard ratios and 95% confidence intervals. Hazard ratios were calculated for each of the four highest quintile groups compared with the first, but also for high CRP categories (90^th or 95^th percentile) versus all others, to study the possible impact of high CRP on VTE. Covariates included previous VTE risk factors measured in the whole ARIC cohort, measured at Visit 4 unless otherwise specified: age (continuous), race (African American, white), sex/hormone replacement therapy (men, women taking HRT, women not taking HRT), diabetes (yes, no), body mass index (continuous), Visit 1 factor VIII_c, and Visit 1 aPTT. Other factors related to CRP (e.g., smoking, lipid levels, physical activity) were not VTE risk factors in ARIC, and thus not included.