An elevated gamma gap as diagnostic tool for acute systemic inflammatory response syndromes in Hospitalized patients

Aims: This study aimed to investigate the predictive value of the Gamma Gap in relation to the probability of cOI positivity. Our secondary objectives aimed to determine the optimal threshold in the Jordanian tested cohort, beyond which negative adverse outcomes would significantly rise. We aimed to assess the sensitivity and specificity of the Gamma gap and compare it with similar previous studies and other related sensitivity indices.
Methods: An observational study was conducted at Prince Rahid bin Al-Hussein Military Hospital to analyze adult medical and surgical patients. The study aimed to examine the prognostic abilities of the evaluated Gamma Gap compared to a combined negative clinical outcome. The study included adult admitted patients with stable baseline renal, liver, hemodynamic, and systemic immune-inflammatory statuses. The study used binary logistic regression to examine the Gamma Gap as an independent variable for predicting adverse clinical outcomes. A sequential statistical analysis of receiver operating characteristic (ROC) testing and sensitivity analysis was conducted to express the predictive utility of the Gamma Gap against the positivity of adverse clinical outcomes. The study also identified the ideal cutoff point for the Gamma Gap, with a value above it indicating a worse outcome and a value below it indicating a better outcome.
Results: A study involving 302 eligible patients was conducted to determine the optimal cutoff point for composite outcomes of interest (cOI). The study found that the higher Gamma Gap group had a stronger positive Pearson correlation coefficient than the lower Gamma Gap group. The unadjusted risk estimate was 29.75 (95% CI; 15.78-56.09). The two genders were distributed equally across the groups, with no statistically significant gender distribution rates. Albumin and albumin to globulin ratio levels showed significantly different distributions between the groups. The ages of the tested patients were insignificantly distributed between the groups. A binary logistic regression model was developed to evaluate 83.4% of cases. The prognosticator Gamma Gap performed as expected, with a p-value of less than 0.001 and an evaluation of 0.902±0.018.
Conclusion: According to our research, hospitalised patients may benefit from keeping their Gamma Gap below 3.04, and this subtracted biochemical tool can be extremely useful in predicting unfavourable clinical outcomes as well as in the screening, diagnosis, and follow-up of both medically and surgically admitted patients.