Trend Analysis and Outcome Prediction in Mechanically Ventilated Patients: A Nationwide Population-Based Study in Taiwan

Objective To investigate the relationship between changes in patient attributes and hospital attributes over time and to explore predictors of medical utilization and mortality rates in mechanical ventilation (MV) patients in Taiwan. Background Providing effective medical care for MV patients is challenging and requires good planning and effective clinical decision making policies. Most studies of MV, however, have only analyzed a single regional ventilator weaning center or respiratory care unit, high-quality population-based studies of MV trends and outcomes are scarce. Methods This population-based cohort study retrospectively analyzed 213,945 MV patients treated during 2004-2009. Results During the study period, the percentages of MV patients with the following characteristics significantly increased: age ≦ 65 years, treatment at a medical center, and treatment by a high-volume physician. In contrast, the percentages of MV patients treated at local hospitals and by low-volume physicians significantly decreased (P<0.001). Age, gender, Deyo-Charlson co-morbidity index, teaching hospital, hospital level, hospital volume, and physician volume were significantly associated with MV outcome (P<0.001). Over the 6-year period analyzed in this study, the estimated mean hospital treatment cost increased 48.8% whereas mean length of stay decreased 13.9%. The estimated mean overall survival time for MV patients was 16.4 months (SD 0.4 months), and the overall in-hospital 1-, 3-, and 5-year survival rates were 61.0%, 36.7%, 17.3%, and 9.6%, respectively. Conclusions These population-based data revealed increases in the percentages of MV patients treated at medical centers and by high-volume physicians, especially in younger patients. Notably, although LOS for MV patients decreased, hospital treatment costs increased. Healthcare providers and patients should recognize that attributes of both the patient and the hospital may affect outcomes.

[1]  D. Wakefield,et al.  Mechanical ventilation in rural ICUs , 1999, Critical care.

[2]  Sangeeta Mehta,et al.  Partial Ventilatory Support Modalities in Acute Lung Injury and Acute Respiratory Distress Syndrome—A Systematic Review , 2012, PloS one.

[3]  Chun-Ta Huang,et al.  Hemoglobin Levels and Weaning Outcome of Mechanical Ventilation in Difficult-To-Wean Patients: A Retrospective Cohort Study , 2013, PloS one.

[4]  E. Livingston,et al.  Procedure volume as a predictor of surgical outcomes. , 2010, JAMA.

[5]  C. Karpman,et al.  Predicting 1-year mortality rate for patients admitted with an acute exacerbation of chronic obstructive pulmonary disease to an intensive care unit: an opportunity for palliative care. , 2014, Mayo Clinic proceedings.

[6]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[7]  R. Richards-Kortum,et al.  A High-Value, Low-Cost Bubble Continuous Positive Airway Pressure System for Low-Resource Settings: Technical Assessment and Initial Case Reports , 2013, PloS one.

[8]  Robert Gray,et al.  A Proportional Hazards Model for the Subdistribution of a Competing Risk , 1999 .

[9]  T. Lagu,et al.  Variation and outcomes associated with direct hospital admission among children with pneumonia in the United States. , 2014, JAMA pediatrics.

[10]  Arthur S Slutsky,et al.  Intensive Care Unit-Acquired Bacteremia in Mechanically Ventilated Patients: Clinical Features and Outcomes , 2013, PloS one.

[11]  Hon-Yi Shi,et al.  Trend Analysis of Hospital Resource Utilization for Prolonged Mechanical Ventilation Patients in Taiwan: A Population-Based Study , 2013, Respiratory Care.

[12]  Salvador Benito,et al.  Characteristics and outcomes in adult patients receiving mechanical ventilation: a 28-day international study. , 2002, JAMA.

[13]  M. Berger,et al.  Frequency and predictors of complications in neurological surgery: national trends from 2006 to 2011. , 2014, Journal of neurosurgery.

[14]  J. Devlin,et al.  Agitation During Prolonged Mechanical Ventilation at a Long-Term Acute Care Hospital , 2014, Journal of intensive care medicine.

[15]  Shannon S Carson,et al.  Increase in tracheostomy for prolonged mechanical ventilation in North Carolina, 1993–2002 , 2004, Critical care medicine.

[16]  K. Nugent,et al.  Factors Influencing the Length of Hospital Stay in Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease Admitted to Intensive Care Units , 2014, Quality management in health care.

[17]  H. Wunsch,et al.  The epidemiology of mechanical ventilation use in the United States* , 2010, Critical care medicine.

[18]  Tze-Wah Kao,et al.  The Impact of Dialysis-Requiring Acute Kidney Injury on Long-Term Prognosis of Patients Requiring Prolonged Mechanical Ventilation: Nationwide Population-Based Study , 2012, PloS one.

[19]  T. Iwashyna,et al.  Hospital Factors Associated With Discharge Bias in ICU Performance Measurement* , 2014, Critical care medicine.

[20]  King-Teh Lee,et al.  In-hospital mortality prediction in patients receiving mechanical ventilation in Taiwan. , 2013, American journal of critical care : an official publication, American Association of Critical-Care Nurses.