Till date several prediction models have been proposed for early detection of high-risk patients susceptible for the development of MODS. These models are crucial for the development of early interventional strategies, planning intensive care resource allocation and risk-stratification of vulnerable patient population to prevent second hit. An initial prediction model of MODS was proposed in 1998, which demonstrated shock parameters, age, ISS and platelet count as the predictor of MODS [18]. Since then efforts have been put to minimize the effect of shock through improved resuscitation strategies. Currently, early hemostatic resuscitation helped in reducing the impact of shock and now it is not considered as a predictor of MODS. A recent study [8] demonstrated age and on-admission platelet count to be independent predictors of MODS. Dewar and colleagues reported that the prediction power could be further enhanced by considering creatinine and bilirubin levels within first 24 h of admission [8]. Ciesla et al. [7] observed that ISS is ineffective in predicting MODS, as there is an injury threshold level above which MODS is likely to occur. Moreover, several investigators have identified advanced age as a marker of comorbidity and consider it as an independent predictor of the development of post-injury MODS in severe trauma patients [7, 63, 72]. An earlier study observed male gender to be an independent predictor of the development of severe infection in surgical patients [73]. Nydam et al. [74] reported thrombocytopenia (<80,000) at 12 h post admission to be an independent predictor of MODS. Consistently, Dewar et al. [8] also observed a similar association showing on-admission thrombocytopenia (<150 × 10⁹/L) to be a significant predictor of MODS. Also, base deficit has been considered as a surrogate marker of hypovolemic shock and is used to identify patients who need early hemostatic resuscitation [75]. Regardless of MODS occurrence, base excess is a strong predictor of mortality in severely injured patients [76]. In addition, base deficit along with other significant predictors of MODS has the potential to stratify high-risk MODS patients initially during admission. A recent prospective cohort study from the USA identified male gender, preexisting liver disease, severe ISS, use of early vasopressors, higher volumes of FFP and crystalloid resuscitation, duration of shock, and more severe depth of shock (lactate values <12 h) to be the independent predictors of MODS [10].

Groeneveld et al. [77] analyzed the utility of immunomodulating interventions for the management of posttraumatic complications. The authors [77] found little significance of immune markers (cytokines) for prognosis of MODS due to lower sensitivity and specificity. Another recent promising approach to predict MODS is the assessment of immune cell functionality. It has been shown that phenotyping of blood Polymorphonuclear granulocytes (PMNs) in post-injury patients help to estimate the kinetics and extent of initial SIRS [78]. Furthermore, it has been observed that prior to sepsis development the PMNs functionality reaches its minimum level, so this approach could be used for determining the high-risk patients at earlier stage [79]. Recently, Anne Morrison et al. [80] demonstrated that trauma patients with hemorrhagic shock who had higher PMN apoptosis are at reduced risk of tissue injury and susceptibility to infection which might lead to MODS.

Based on the available predictors, to some extent MODS can be predicted at early stage after injury and so early prevention and treatment could be initiated at this point [18]. Particularly, a reliable prediction model might be supportive for patient recruitment and inclusion in trauma studies, in which MODS marks the primary end point. Finally, identification of major risk factors and predictors helps to stratify and enroll patients for clinical trials during the initial phase of trauma care and surgery. It also supports the assessment of quantifiable outcome of trials in critical ill patients. Although, numerous studies have focused on MODS, majority of prediction models have restricted applicability which may be attributed to the genetic variation among the individual trauma and surgery patients. Therefore, current studies based on genetic predisposition of multiple trauma and surgery patients will favorably improve these prediction models and will enhance their applicability in the near future [81].

Over the years extensive clinical and experimental studies have been performed for early detection and treatment of post-injury MODS, however it remains a challenging issue for the emergency physicians and trauma surgeons. The current management strategies of high-risk MODS patients are mainly based on preventive and therapeutic interventions.