Description

Background and rationale for the study Septic shock patients and DIC commonly coexist and progression to overt DIC is serial process. Sepsis and septic shock condition is a prevalent condition as studied by Stephen et al especially in low medium income countries with incidence of 31.5 million per year. Divatia JV et al found incidence of severe sepsis and septic shock 28.3% in study covering different ICUs in India. Rhee C et al found incidence of severe sepsis and septic shock as high as 52.8%. Marx,G., et al observed incidence of septic shock in German ICUs to be 12.6 % whereas Mulatu HA et al found it to be 26.5% in African ICUs.

Also, septic shock has very high mortality rates. In India, Divatia JV et al observed that mortality in septic shock patients was 53.4 % and Chatterjee et al observed it to be 62.8%. Mortality rate according to different geographical locations have variations but still consistently high: 22.8% mortality in Greece ICUs, 79% in Turkish ICUs observed by Baykara et al in Japan 27%, in Taiwan 43.8%, in China 51.9 %.

Disseminated intravascular coagulation (DIC) is prevalent entity in sepsis/septic shock patients as observed in different studies: Ko BS et al observed prevalence to be 17.6%, Dhainut J.F et al found it to be 28.9%, Saito et al in Japan to be 29% and J Kienast et al in Germany observed it to be 40.7%.

DIC itself has high mortality: 29.1%, 40.7%, 50% and as high as 56%. Mortality rates further increases when DIC co exists with severe sepsis as seen 67.6% by Ogura H et al, 44.6% vs. 55.3 % without and with DIC respectively seen by Hayakawa et al, 11.7% vs. 54.1% without and with DIC respectively seen by Solanki D et al .

Septic shock patients are at high risk of develop multiple organ dysfunction (MODS). In fact, both DIC score and organ dysfunction were found increased in patients with septic shock as compared to patients without septic shock so the resultant higher mortality and MODS. Studies also found mortality risk further increases in septic shock patients with the presence of DIC.

Methodology Study design: This prospective observational study will be conducted at the Department of Critical Care Medicine in collaboration with the Department of Haematology, SGPGIMS, Lucknow after the approval from the Institutional Ethics Committee (IEC) Study protocol: During the study period, all adult ICU participants with the diagnosis of septic shock will be considered, as per inclusion and exclusion criteria, DIC scores and SOFA scores will be calculated and followed-up for the 14 days.

Definition and scores: Septic shock is defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Participants with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mmHg or greater and serum lactate level greater than 2mmol/L in the absence of hypovolemia (Sepsis -3 recommendations). DIC score for overt and non-overt DIC will be used as per International Society on Thrombosis and Haemostasis. (ISTH) Sample collection for DIC score calculation Blood samples will be collected as below Baseline sampling : At inclusion Second sampling : At 72 hours ±12 hours Third sampling : After 72 hours (±12 hours) of second sampling. Data collections: Demographic and relevant clinical characteristics of included participants will be collected on structured case report form.

Sample size and statistical analysis: Based on the study conducted by the H Ogura et al. (2014), SOFA score was during the day 1 (10.7±3.8) to day 4 (8.9±5.0) [Change in score: Cohen d effect size =0.398). At minimum two-sided 95% confidence and 80% power of the study, minimum estimated sample size for the study is 52. Finally minimum 60 participants to be enrolled in the study. Sample size was estimated using software G*power version 3.1.9.7. Descriptive statistics of the continuous variables will be presented as mean ± SD / Median (IQR) whereas categorical variables in Frequency (%). To compare the observations between baseline to follow-up data (quantitative variable), with the outcomes, two-way repeated measures ANOVA will be used. One way Analysis of covariance to be used to compare the post observations into outcomes after the adjusting the baseline measurements. Change in the SOFA score with change in the DIC score to be compared using spearman rank correlation coefficient. Decision trees analysis including Classification and regression trees to be used to identify the factors and subgroups predicting the outcomes. General linear regression model to be used to identify the factors predicting the change in the SOFA score. A p value < 0.05 to be considered as statistically significant. Statistical analysis to be performed using software "Statistical package for social sciences version 23 (SPSS-23) and MedCalc.