The association between intraoperative low driving pressure ventilation and perioperative healthcare-associated costs: A retrospective multicenter cohort study.

STUDY OBJECTIVE: A low dynamic driving pressure during mechanical ventilation for general anesthesia has been associated with a lower risk of postoperative respiratory complications (PRC), a key driver of healthcare costs. It is, however, unclear whether maintaining low driving pressure is clinically relevant to measure and contain costs. We hypothesized that a lower dynamic driving pressure is associated with lower costs. DESIGN: Multicenter retrospective cohort study. SETTING: Two academic healthcare networks in New York and Massachusetts, USA. PATIENTS: 46,715 adult surgical patients undergoing general anesthesia for non-ambulatory (inpatient and same-day admission) surgery between 2016 and 2021. INTERVENTIONS: The primary exposure was the median intraoperative dynamic driving pressure. MEASUREMENTS: The primary outcome was direct perioperative healthcare-associated costs, which were matched with data from the Healthcare Cost and Utilization Project-National Inpatient Sample (HCUP-NIS) to report absolute differences in total costs in United States Dollars (US$). We assessed effect modification by patients' baseline risk of PRC (score for prediction of postoperative respiratory complications [SPORC] ≥ 7) and effect mediation by rates of PRC (including post-extubation saturation < 90%, re-intubation or non-invasive ventilation within 7 days) and other major complications. MAIN RESULTS: The median intraoperative dynamic driving pressure was 17.2cmH2O (IQR 14.0-21.3cmH2O). In adjusted analyses, every 5cmH2O reduction in dynamic driving pressure was associated with a decrease of -0.7% in direct perioperative healthcare-associated costs (95%CI -1.3 to -0.1%; p = 0.020). When a dynamic driving pressure below 15cmH2O was maintained, -US$340 lower total perioperative healthcare-associated costs were observed (95%CI -US$546 to -US$132; p = 0.001). This association was limited to patients at high baseline risk of PRC (n = 4059; -US$1755;97.5%CI -US$2495 to -US$986; p < 0.001), where lower risks of PRC and other major complications mediated 10.7% and 7.2% of this association (p < 0.001 and p = 0.015, respectively). CONCLUSIONS: Intraoperative mechanical ventilation targeting low dynamic driving pressures could be a relevant measure to reduce perioperative healthcare-associated costs in high-risk patients.

Risk factors for hospitalization among community-dwelling primary care older patients: development and validation of a predictive model.

BACKGROUND: Unplanned hospitalization often represents a costly and hazardous event for the older population. OBJECTIVES: To develop and validate a predictive model for unplanned medical hospitalization from administrative data. RESEARCH DESIGN: Model development and validation. SUBJECTS: A total of 3919 patients aged > or =70 years who were followed for at least 1 year in primary care clinics of an academic medical center. MEASURES: Risk factor data and the primary outcome of unplanned medical hospitalization were obtained from administrative data. RESULTS: Of 1932 patients in the development cohort, 299 (15%) were hospitalized during 1 year follow up. Five independent risk factors were identified in the preceding year: Deyo-Charlson comorbidity score > or =2 [adjusted relative risk (RR) = 1.8; 95% confidence interval (CI): 1.4-2.2], any prior hospitalization (RR = 1.8; 95% CI: 1.5-2.3), 6 or more primary care visits (RR = 1.6; 95% CI: 1.3-2.0), age > or =85 years (RR = 1.4; 95% CI: 1.1-1.7), and unmarried status (RR = 1.4; 95% CI: 1.1-1.7). A risk stratification system was created by adding 1 point for each factor present. Rates of hospitalization for the low- (0 factor), intermediate- (1-2 factors), and high-risk (> or =3 factors) groups were 5%, 15%, and 34% (P < 0.0001). The corresponding rates in the validation cohort, where 328/1987 (17%) were hospitalized, were 6%, 16%, and 36% (P < 0.0001). CONCLUSIONS: A predictive model based on administrative data has been successfully validated for prediction of unplanned hospitalization. This model will identify patients at high risk for hospitalization who may be candidates for preventive interventions.