Preoperative Thoracic CT Findings Associated With Postoperative Mechanical Ventilation in Patients Undergoing Major Abdominal or Pelvic Surgery: A Matched Case-Control Study.

BACKGROUND. Postoperative prolonged mechanical ventilation is associated with increased morbidity and mortality. Reliable predictors of the need for postoperative mechanical ventilation after abdominal or pelvic surgeries are lacking. OBJECTIVE. The purpose of this study was to explore associations between preoperative thoracic CT findings and the need for postoperative mechanical ventilation after major abdominal or pelvic surgeries. METHODS. This retrospective case-control study included patients who underwent abdominal or pelvic surgeries during the period from January 1, 2014, through December 31, 2018, and had undergone preoperative thoracic CT. Case patients were patients who required postoperative mechanical ventilation. Control patients and case patients were matched at a 3:1 ratio on the basis of age, sex, body mass index, chronic obstructive pulmonary disease, smoking status, and surgery type. Two radiologists (readers 1 and 2) reviewed the CT images. Findings were compared between groups. RESULTS. The study included 165 patients (70 women, 95 men; mean age, 67.0 ± 9.7 [SD] years; 42 case patients and 123 matched control patients). Bronchial wall thickening and pericardial effusion were more frequent in case patients than control patients for reader 2 (10% vs 2%, p = .03; 17% vs 5%, p = .01) but not for reader 1. Pulmonary artery diameter (mean ± SD) was greater in case patients than control patients for reader 2 (2.9 ± 0.5 cm vs 2.8 ± 0.5 cm, p = .045) but not reader 1. Right lung height was lower in case patients than control patients for reader 1 (18.4 ± 2.9 cm vs 19.9 ± 2.7 cm, p = .01) and reader 2 (18.3 ± 2.9 cm vs 19.8 ± 2.7 cm, p = .01). Left lung height was lower in case patients than control patients for reader 1 (19.5 ± 3.1 cm vs 21.1 ± 2.6 cm, p = .01) and reader 2 (19.6 ± 2.4 cm vs 20.9 ± 2.6 cm, p = .01). Anteroposterior (AP) chest diameter was greater for case patients than control patients for reader 1 (14.0 ± 2.3 cm vs 12.9 ± 3.7 cm, p = .02) and reader 2 (14.2 ± 2.2 cm vs 13.2 ± 3.6 cm, p = .04). In a multivariable regression model using pooled reader data, bronchial wall thickening exhibited an odds ratio (OR) of 4.6 (95% CI, 1.3-16.5; p = .02); pericardial effusion, an OR of 5.1 (95% CI, 1.7-15.5; p = .004); pulmonary artery diameter, an OR of 1.4 per 1-cm increase (95% CI, 0.7-3.0; p = .32); mean lung height, an OR of 0.8 per 1-cm increase (95% CI, 0.7-1.001; p = .05); and AP chest diameter, an OR of 1.2 per 1-cm increase (95% CI, 1.013-1.4; p = .03). CONCLUSION. CT features are associated with the need for postoperative mechanical ventilation after abdominal or pelvic surgery. CLINICAL IMPACT. Many patients undergo thoracic CT before abdominal or pelvic surgery; the CT findings may complement preoperative clinical risk factors.

A preoperative predictive model for prolonged post-anaesthesia care unit stay after outpatient surgeries.

Study objective: To create a preoperative predictive model for prolonged post-anaesthesia care unit (PACU) stay for outpatient surgery and compare with an existing (University of California-San Diego, UCSD) model. Design: Retrospective observational study. Setting: Post-anaesthesia care unit. Patients: Outpatient surgical patients discharged on the same day in a large academic institution. Preoperative data were collected. The study period was three months in 2016. Measurements: Prolonged PACU stay defined as a length of stay longer than the third quartile. We utilized multivariate regression analyses and bootstrapping statistical techniques to create a predictive model for prolonged PACU stay. Main results: Four strong predictors for prolonged PACU stay: general anaesthesia, obstructive sleep apnoea, surgical specialty and scheduled case duration. Our model had an excellent discrimination performance and a good calibration. Conclusion: We developed a predictive model for prolonged PACU stay in our institution. This model is different from the UCSD model probably secondary to local and regional differences in outpatient surgery practice. Therefore, individual practice study outcomes may not apply to other practices without careful consideration of these differences.

Agile Co-Development for Clinical Adoption and Adaptation of Innovative Technologies.

Even the most innovative healthcare technologies provide patient benefits only when adopted by clinicians and/or patients in actual practice. Yet realizing optimal positive impact from a new technology for the widest range of individuals who would benefit remains elusive. In software and new product development, iterative rapid-cycle "agile" methods more rapidly provide value, mitigate failure risks, and adapt to customer feedback. Co-development between builders and customers is a key agile principle. But how does one accomplish co-development with busy clinicians? In this paper, we discuss four practical agile co-development practices found helpful clinically: (1) User stories for lightweight requirements; (2) Time-boxed development for collaborative design and prompt course correction; (3) Automated acceptance test driven development, with clinician-vetted specifications; and (4) Monitoring of clinician interactions after release, for rapid-cycle product adaptation and evolution. In the coming wave of innovation in healthcare apps ushered in by open APIs to EHRs, learning rapidly what new product features work well for clinicians and patients will become even more crucial.