Validation of the Prognosis for Prolonged Ventilation (ProVent) score in patients receiving 14days of mechanical ventilation

Development of a Prognostic Scoring System for Tracheostomized Patients Requiring Prolonged Ventilator Care: A Ten-Year Experience in a University-Affiliated Tertiary Hospital

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 × 103/μL, PaO2/FiO2 < 200 mmHg, body mass index (BMI) < 23.0 kg/m2, 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.

Ability of the modified NUTRIC score to predict mortality in patients requiring short-term versus prolonged acute mechanical ventilation: a retrospective cohort study

BACKGROUND

The modified NUTRIC (nutritional risk in the critically ill) score has been reported to predict clinical outcomes in critically ill patients. However, the applicability of this score may differ between patients undergoing short-term mechanical ventilation (STMV, < 96 h) and those undergoing prolonged acute mechanical ventilation (PAMV, ⩾96 h), as PAMV patients typically experience significantly higher morbidity and mortality.

OBJECTIVE

This study aimed to investigate the predictive ability of modified NUTRIC score for predicting 28-day mortality in patients receiving STMV and PAMV.

DESIGN

Retrospective single-center cohort study.

METHODS

We enrolled patients who received mechanical ventilation (MV) on the day of admission to the intensive care unit (ICU) from 1 December 2015 to 30 November 2020. Modified NUTRIC scores were calculated based on the clinical data of each patient at ICU admission.

RESULTS

The study population comprised 464 patients, including 319 (68.8%) men with a mean age of 69.7 years. Among these patients, 132 (28.4%) received STMV and 332 (71.6%) received PAMV. The overall 28-day mortality rate was 26.7%, which was significantly higher in STMV patients than in PAMV patients (37.9% versus 22.3%, p < 0.001). Evaluation of the predictive performance of the modified NUTRIC score for 28-day mortality revealed areas under the receiver operating characteristic curves of 0.672 [95% confidence interval (CI): 0.627-0.714] for total patients, 0.819 (95% CI, 0.742-0.880) for STMV patients, and 0.595 (95% CI, 0.540-0.648) for PAMV patients. The best overall cutoff value was 5 in total, STMV, and PAMV patients. This cutoff value was a significant predictor of 28-day mortality based on the Cox proportional hazard model for total [hazards ratio (HR): 2.681; 95% CI: 1.683-4.269] and STMV (HR: 5.725; 95% CI: 2.057-15.931) patients, but not for PAMV patients.

CONCLUSION

The modified NUTRIC score is more effective in predicting 28-day mortality in patients undergoing STMV than in those undergoing PAMV.

A Predictive Model for Dysphagia after Ventilator Liberation in Severe Pneumonia Patients Receiving Tracheostomy: A Single-Center, Observational Study

The main purpose of this study was to develop a model predictive of dysphagia in hospital survivors with severe pneumonia who underwent tracheostomy during their hospital stay. The present study included 175 patients (72% male; mean age, 71.3 years) over 5 years. None of these patients had a history of deglutition disorder before hospital admission. Binary logistic regression analysis was performed to identify factors predicting dysphagia at hospital discharge. Dysphagia scores were calculated from β-coefficients and by assigning points to variables. Of the enrolled patients, 105 (60%) had dysphagia at hospital discharge. Factors prognostic of dysphagia at hospital discharge included being underweight (body mass index < 18.5 kg/m2), non-participation in a dysphagia therapy program, mechanical ventilation ≥ 15 days, age ≥ 74 years, and chronic neurologic diseases. Underweight and non-participation in a dysphagia therapy program were assigned +2 points and the other factors were assigned +1 point. Dysphagia scores showed acceptable discrimination (area under the receiver operating characteristic curve for dysphagia 0.819, 95% confidence interval: 0.754−0.873, p < 0.001) and calibration (Hosmer−Lemeshow chi-square = 9.585, with df 7 and p = 0.213). The developed dysphagia score was predictive of deglutition disorder at hospital discharge in tracheostomized patients with severe pneumonia.

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation

Background

Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.

Methods

Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.

Results

There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.

Discussion

We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.

Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

BACKGROUND

The aim of the present study was to develop a prognostic model using demographic characteristics, comorbidities, and clinical variables measured on day 4 of mechanical ventilation (MV) for patients with prolonged acute mechanical ventilation (PAMV; MV for >96 hours).

METHODS

Data from 437 patients (70.9% male; median age, 68 years) were obtained over a period of 9 years. All patients were diagnosed with pneumonia. Binary logistic regression identified factors predicting mortality at 90 days after the start of MV. A PAMV prognosis score was calculating ß-coefficient values and assigning points to variables.

RESULTS

The overall 90-day mortality rate was 47.1%. Five factors (age ≥65 years, body mass index <18.5 kg/m2, hemato-oncologic diseases as comorbidities, requirement for vasopressors on day 4 of MV and requirement for neuromuscular blocking agents on day 4 of MV) were identified as prognostic indicators. Each factor was valued as +1 point, and used to develop a PAMV prognosis score. This score showed acceptable discrimination (area under the receiver operating characteristic curve of 0.695 for mortality, 95% confidence interval 0.650-0.738, p<0.001), and calibration (Hosmer-Lemeshow chi-square=6.331, with df 7 and p=0.502). The cutoff value for predicting mortality based on the maximum Youden index was ≤2 (sensitivity, 87.5%; specificity, 41.3%). For patients with PAMV scores ≤1, 2, 3 and ≥4, the 90-day mortality rates were 29.2%, 45.7%, 67.9%, and 90.9%, respectively (P<0.001).

CONCLUSIONS

Our study developed a PAMV prognosis score for predicting 90-day mortality. Further research is needed to validate the utility of this score.

Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study

Rationale

Prediction of patients at risk for mortality can help triage patients and assist in resource allocation.

Objectives

Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients.

Methods

Retrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality.

Results

When trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows.

Conclusions

This machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.

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Mortality predictions have not previously been evaluated for COVID-19 patients.

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Machine learning may be a useful predictive tool for anticipating patient mortality.

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Prediction can be estimated at clinically useful windows up to 72 h in advance.

Factors predicting long-term survival of patients with sepsis on arrival at the emergency department: A single-center, observational study

Predicting long-term outcomes after sepsis is important when caring for patients with this condition. The purpose of the present study was to develop models predicting long-term mortality of patients with sepsis, including septic shock.Retrospective data from 446 patients with sepsis (60.8% men; median age, 71 years) treated at a single university-affiliated tertiary care hospital over 3 years were reviewed. Binary logistic regression was used to identify factors predicting mortality at 180 and 365 days after arrival at the emergency department. Long-term prognosis scores for the 180- and 365-day models were calculated by assigning points to variables according to their β coefficients.The 180- and 365-day mortality rates were 40.6% and 47.8%, respectively. Multivariate analysis identified the following factors for inclusion in the 180- and 365-day models: age ≥65 years, body mass index ≤18.5 kg/m, hemato-oncologic diseases as comorbidities, and ventilator care. Patients with scores of 0 to ≥3 had 180-day survival rates of 83.8%, 70.8%, 42.3%, and 25.0%, respectively, and 365-day survival rates of 72.1%, 64.6%, 36.2%, and 15.9%, respectively (all differences P < .001; log-rank test). The areas under the receiver operating characteristic curves of the 180- and 365-day models were 0.713 (95% confidence interval [CI] 0.668-0.756, P < .001) and 0.697 (95% CI 0.650-0.740, P < .001), respectively.These long-term prognosis models based on baseline patient characteristics and treatments are useful for predicting the 6- and 12-month mortality rates of patients with sepsis.

Association between Medical Costs and the ProVent Model in Patients Requiring Prolonged Mechanical Ventilation

BACKGROUND

The purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]).

METHODS

Retrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors.

RESULTS

The mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (γ) (γ=0.367, p<0.001).

CONCLUSION

The ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.