Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection

Abstract

Background Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study

“…To date, several studies have identified a link between the respiratory rate and worse outcomes [ 25 ]. Additionally, we found that urine output was associated with better outcomes, which was consistent with those of previous studies [ 26 ]. In the present study, age was shown to be a predictor of mortality in critically ill patients with sepsis.…”

Predictive value of neutrophil-to-lymphocyte and platelet ratio in in-hospital mortality in septic patients

“…Many studies have shown that NLR, PLR are inflammatory indicators, independent risk factors and prognostic predictors of sepsis, which is consistent with our findings [ 23 , 24 , 25 , 26 , 27 ]. Also, Platelets have also been shown to be associated with the severity and prognosis of sepsis [ 28 ].…”

Ceftazidime/Avibactam-Based Versus Polymyxin B-Based Therapeutic Regimens for the Treatment of Carbapenem-Resistant Klebsiella pneumoniae Infection in Critically Ill Patients: A Retrospective Cohort Study

“…On the one hand, surviving patients have better renal function than patients in the dead group according to the comparison of CrCl, which is related to a lower demand on CRRT. On the other hand, some clinical studies indicate that CRRT might not be beneficial to effectively lower mortality for infected patients or those with sepsis [26,27].…”