Development of a Prognostic Scoring System for Tracheostomized Patients Requiring Prolonged Ventilator Care: A Ten-Year Experience in a University-Affiliated Tertiary Hospital.
Hyojin JangWanho YooHayoung SeongSaerom KimSoo Han KimEun-Jung JoJung Seop EomKwangha LeePublished in: Medicina (Kaunas, Lithuania) (2024)
Background and Objectives : This study aimed to assess the value of a novel prognostic model, based on clinical variables, comorbidities, and demographic characteristics, to predict long-term prognosis in patients who received mechanical ventilation (MV) for over 14 days and who underwent a tracheostomy during the first 14 days of MV. Materials and Methods : Data were obtained from 278 patients (66.2% male; median age: 71 years) who underwent a tracheostomy within the first 14 days of MV from February 2011 to February 2021. Factors predicting 1-year mortality after the initiation of MV were identified by binary logistic regression analysis. The resulting prognostic model, known as the tracheostomy-ProVent score, was computed by assigning points to variables based on their respective ß-coefficients. Results : The overall 1-year mortality rate was 64.7%. Six factors were identified as prognostic indicators: platelet count < 150 × 10 3 /μL, PaO 2 /FiO 2 < 200 mmHg, body mass index (BMI) < 23.0 kg/m 2 , albumin concentration < 2.8 g/dL on day 14 of MV, chronic cardiovascular diseases, and immunocompromised status at admission. The tracheostomy-ProVent score exhibited acceptable discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.786 (95% confidence interval: 0.733-0.833, p < 0.001) and acceptable calibration (Hosmer-Lemeshow chi-square: 2.753, df: 8, p = 0.949). Based on the maximum Youden index, the cut-off value for predicting mortality was set at ≥2, with a sensitivity of 67.4% and a specificity of 76.3%. Conclusions : The tracheostomy-ProVent score is a good predictive tool for estimating 1-year mortality in tracheostomized patients undergoing MV for >14 days. This comprehensive model integrates clinical variables and comorbidities, enhancing the precision of long-term prognosis in these patients.
Keyphrases
- mechanical ventilation
- end stage renal disease
- body mass index
- chronic kidney disease
- newly diagnosed
- ejection fraction
- healthcare
- acute respiratory distress syndrome
- cardiovascular disease
- risk factors
- intensive care unit
- magnetic resonance imaging
- prognostic factors
- cardiovascular events
- machine learning
- type diabetes
- computed tomography
- physical activity
- quality improvement
- patient reported outcomes
- peripheral blood
- ionic liquid
- magnetic resonance
- contrast enhanced
- diffusion weighted imaging