BACKGROUND: Significant mortality after gastrostomy insertion remains and some risk factors have been identified, but no predictive scoring system exists. OBJECTIVE: To identify risk factors for mortality, formulate a predictive scoring system, and validate the score. Comparison to an artificial neural network (ANN). DESIGN: Endoscopic database analysis. SETTING: Six hospitals (2 teaching hospitals) in the South Yorkshire region, United Kingdom. PATIENTS: This study involved all patients referred for gastrostomy insertion. INTERVENTION: Generation of clinical scores to predict 30-day mortality in patients undergoing gastrostomy insertion. MAIN OUTCOME MEASUREMENTS: Risk factors for 30-day mortality. Internal and external validation of the score. Comparison with an ANN. RESULTS: Univariate analysis showed that 30-day mortality was associated with age, albumin levels, and cardiac and neurological comorbidities. Multivariate analysis showed that only age and albumin levels were independent. Modeling provided scores of 0, 1, 2, and 3 corresponding to 30-day mortalities of 0% (0-2.1), 7% (2.9-13.9), 21.3% (13.5-30.9), and 37.3% (24.1-51.9), respectively. Application of the scoring system at the other teaching hospital and the 4 district general hospitals gave 30-day mortality rates that were not significantly different from those predicted. Receiver operating characteristic curves for the score and the ANN were comparable. LIMITATIONS: Nonrandomized study. Score not used as a decision-making tool. CONCLUSION: The gastrostomy score provides an estimate of 30-day mortality for patients (and their relatives) when gastrostomy insertion is being discussed. This score requires evaluation as a decision-making tool in clinical practice. ANN analysis results were similar to the outcomes from the clinical score.
BACKGROUND: Significant mortality after gastrostomy insertion remains and some risk factors have been identified, but no predictive scoring system exists. OBJECTIVE: To identify risk factors for mortality, formulate a predictive scoring system, and validate the score. Comparison to an artificial neural network (ANN). DESIGN: Endoscopic database analysis. SETTING: Six hospitals (2 teaching hospitals) in the South Yorkshire region, United Kingdom. PATIENTS: This study involved all patients referred for gastrostomy insertion. INTERVENTION: Generation of clinical scores to predict 30-day mortality in patients undergoing gastrostomy insertion. MAIN OUTCOME MEASUREMENTS: Risk factors for 30-day mortality. Internal and external validation of the score. Comparison with an ANN. RESULTS: Univariate analysis showed that 30-day mortality was associated with age, albumin levels, and cardiac and neurological comorbidities. Multivariate analysis showed that only age and albumin levels were independent. Modeling provided scores of 0, 1, 2, and 3 corresponding to 30-day mortalities of 0% (0-2.1), 7% (2.9-13.9), 21.3% (13.5-30.9), and 37.3% (24.1-51.9), respectively. Application of the scoring system at the other teaching hospital and the 4 district general hospitals gave 30-day mortality rates that were not significantly different from those predicted. Receiver operating characteristic curves for the score and the ANN were comparable. LIMITATIONS: Nonrandomized study. Score not used as a decision-making tool. CONCLUSION: The gastrostomy score provides an estimate of 30-day mortality for patients (and their relatives) when gastrostomy insertion is being discussed. This score requires evaluation as a decision-making tool in clinical practice. ANN analysis results were similar to the outcomes from the clinical score.
Authors: Diego L Lima; Luiz Eduardo C Miranda; Marcel Rolland Ciro da Penha; Raquel N C L Lima; Dalmir Cavalcanti Dos Santos; Matheus Stillner Eufrânio; Ana Clara G Miranda; Leila Maria Moreira Beltrão Pereira Journal: JSLS Date: 2021 Jul-Sep Impact factor: 2.172