Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication.

How to cite this article: Patnaik RK, Karan N. Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication. Indian J Crit Care Med 2024;28(6):529-530.

Outcome in patients with open abdomen treatment for peritonitis: a multidomain approach outperforms single domain predictions.

Numerous patient-related clinical parameters and treatment-specific variables have been identified as causing or contributing to the severity of peritonitis. We postulated that a combination of clinical and surgical markers and scoring systems would outperform each of these predictors in isolation. To investigate this hypothesis, we developed a multivariable model to examine whether survival outcome can reliably be predicted in peritonitis patients treated with open abdomen. This single-center retrospective analysis used univariable and multivariable logistic regression modeling in combination with repeated random sub-sampling validation to examine the predictive capabilities of domain-specific predictors (i.e., demography, physiology, surgery). We analyzed data of 1,351 consecutive adult patients (55.7% male) who underwent open abdominal surgery in the study period (January 1998 to December 2018). Core variables included demographics, clinical scores, surgical indices and indicators of organ dysfunction, peritonitis index, incision type, fascia closure, wound healing, and fascial dehiscence. Postoperative complications were also added when available. A multidomain peritonitis prediction model (MPPM) was constructed to bridge the mortality predictions from individual domains (demographic, physiological and surgical). The MPPM is based on data of n = 597 patients, features high predictive capabilities (area under the receiver operating curve: 0.87 (0.85 to 0.90, 95% CI)) and is well calibrated. The surgical predictor "skin closure" was found to be the most important predictor of survival in our cohort, closely followed by the two physiological predictors SAPS-II and MPI. Marginal effects plots highlight the effect of individual outcomes on the prediction of survival outcome in patients undergoing staged laparotomies for treatment of peritonitis. Although most single indices exhibited moderate performance, we observed that the predictive performance was markedly increased when an integrative prediction model was applied. Our proposed MPPM integrative prediction model may outperform the predictive power of current models.

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost.

BACKGROUND: Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. METHODS: Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. RESULTS: A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800-0.838], 0.797 [95% CI 0.781-0.813] and 0.857 [95% CI 0.839-0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. CONCLUSIONS: Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

The impact of rescue or maintenance therapy with EGFR TKIs for Stage IIIb-IV non-squamous non-small-cell lung cancer patients requiring mechanical ventilation.

BACKGROUND: The toxicity of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is less than that of cytotoxic agents. The reports of dramatic response and improvement in performance status with the use of EGFR TKIs may influence a physician's decision-making for patients with non-squamous non-small cell lung cancer (NSCLC) and life-threatening respiratory distress. The aim of this study was to evaluate the outcome of rescue or maintenance therapy with EGFR TKI for stage IIIb-IV non-squamous NSCLC patients requiring mechanical ventilation. METHODS: Eighty-three Asian patients with stage IIIb-IV non-squamous NSCLC and who required mechanical ventilation between June 2005 and January 2010 were evaluated. RESULTS: Of the 83 patients, 16 (19%) were successfully weaned from the ventilator. The use of EGFR TKI as rescue or maintenance therapy during respiratory failure did not improve the rate of successful weaning (standard care 18% vs. with EGFR TKI, 22%; p = 0.81) in univariate and multivariate analyses. CONCLUSIONS: Rescue or maintenance therapy with EGFR TKI for stage IIIb-IV non-squamous NSCLC patients requiring mechanical ventilation was not associated with better outcome. An end-of-life discussion should be an important aspect in the care of this group of patients, since only 19% were successfully weaned from mechanical ventilation.