Risk-Predictors of Mortality in Hospitalized Patients with Thrombotic Thrombocytopenic Purpura: Nationally Representative Data from 2007-2011
Authors: Goel Ruchika , Ness Paul , Takemoto Clifford M. , King Karen E. , Tobian Aaron 
Published: October 2019

Introduction: Survivors of Thrombotic Thrombocytopenic Purpura (TTP) hospitalizations have been proposed to be at higher risk for long term poor clinical outcomes and premature death. Patients with TTP have a high risk for in-hospital morbidity and mortality as well. However, there is a paucity of data on the predictors of adverse outcomes including death in hospitalized patients with TTP.

Methods: A weighted analysis of 5 years (2007-2011) using data from the Nationwide Inpatient Sample, a stratified probability sample of 20% of all hospital discharges among community hospitals in the United States (approximately 1100 hospitals/year), was performed. Hospitalizations with TTP as the primary admitting diagnoses were identified using the ICD-9 discharge code 446.6. Univariate and stepwise multivariable logistic regression analyses with elimination were used for statistical analysis. Based on results of univariate analysis, the significant variables were added in a stepwise manner in a multivariable model. All variables selected for the multivariable model were tested for interaction with a significance threshold level of p<0.2. Except for this, all hypothesis testing was two tailed and p<0.05 was considered significant. Receiver Operator Characteristics (ROC) curve was constructed using risk factors on multivariate analysis.

Results: The all-cause mortality rate was 8.7% (918/10615) among admissions with primary diagnosis of TTP (0.5% pediatric, 65.9% female, 58.2% Caucasian, 27.2% African-American). Table 1 lists the risk factors by univariate analysis and includes a) factors with significantly higher odds of mortality and b) other putative factors which were not statistically significant predictors.

Table 2: In stepwise multivariable logistic regression analysis: arterial thrombosis (adjOR 5.1 95%CI=1.1-31.7), acute myocardial infarction (adjOR 2.8, 95%CI=1.6-4.9), non-occurrence of either intervention: plasmapheresis or fresh frozen plasma infusion (adjOR 2.0, 95% CI=1.4-2.9) 4) requirement of platelet transfusions during hospitalization (adjOR 2.0, 95%CI= 1.3-3.2) and every ten year increase in age (OR 1.4 95%CI=1.3-1.6) were independently predictive of mortality in TTP patients (area under the curve for ROC 74%, Figure 1).

Conclusion: We present a set of independent risk factors that may potentially be used in a predictive model of mortality in TTP. Early and targeted aggressive therapy based on these factors should guide the management of hospitalized patients with TTP for improved outcomes. Table 1.Unadjusted odds of in-hospital mortality.Significant predictors of mortality for TTP on univariate analysisOdds Ratio95% Confidence LimitsArterial Thrombosis 10.92.254.6AMI 3.72.16.2STROKE 4.93.07.9Platelet Transfusion 2.31.53.6Bleeding event 1.71.12.6Plasmapheresis (No vs. Yes)1.61.22.3plasmapheresis or plasma infusion (not performed)2.21.53.1Every 10 years increase in age1.51.31.6PRBC transfusion1.71.22.3Caucasian versus African American1.91.32.8Asian versus African American3.31.29.1V ariables not significant predictors of mortality for TTP on univariate analysis.Odds Ratio95% Confidence LimitsVenous Thrombosis/Thromboembolism1.90.84.4FEMALE versus male gender1.00.71.4Hypertension Yes vs. no0.90.61.2Diabetes Yes vs. no0.90.61.4Chronic Kidney Disease Yes vs. No1.40.92.2End Stage Renal Disease Yes vs. No0.90.41.9Overweight/Obese Yes vs. No0.70.41.5Variables meeting criteria for inclusion in multiple logistic regression model are in boldface type.

Table 2. Multivariable Predictors for In Hospital Mortality in patients with primary diagnosis of TTP Adjusted Odds Ratio 95% Confidence Limits Arterial Thrombosis 6.0 1.2 30.5 Acute myocardial infarction 2.8 1.6 4.8 No Plasmapheresis/Plasma infusion 2.0 1.4 2.9 Platelet Transfusion 2.1 1.4 3.2 Age (per 10 year higher) 1.4 1.3 1.6 Female versus Male 1.2 0.8 1.7 TTP = Thrombotic Thrombocytopenic Purpura

Step 0: Using arterial thrombosis

Figure 1: Receiver- Operator-Characteristic Curve (ROC) overlay curve for the stepwise multivariable logistic regression risk prediction showing incremental AUC with addition of each risk factor for hospital patients with TTP. Figure 1:. Receiver- Operator-Characteristic Curve (ROC) overlay curve for the stepwise multivariable logistic regression risk prediction showing incremental AUC with addition of each risk factor for hospital patients with TTP.

Step 1: Adding acute myocardial infarction

Step 2: Adding plasmapheresis /fresh frozen plasma infusion

Step 3: Adding platelet transfusions

Final model: Adding every ten year increase in age.

Disclosures

Ness: Terumo BCT: Consultancy.