Pendleton, Kathryn M.

OBJECTIVES: We sought to identify factors affecting physicians’ cognition and clinical behavior when evaluating patients that may need fluid therapy. BACKGROUND: Proponents of dynamic fluid responsiveness testing advocate measuring cardiac output or stroke volume after a maneuver to prove that further fluids will increase cardiac output. However, surveys suggest that fluid therapy in clinical practice is often given without prior responsiveness testing. DESIGN: Thematic analysis of face-to-face structured interviews. SETTING: ICUs and medical-surgical wards in acute care hospitals. SUBJECTS: Intensivists and hospitalist physicians. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We conducted 43 interviews with experienced physicians in 19 hospitals. Hospitalized patients with hypotension, tachycardia, oliguria, or elevated serum lactate are commonly seen by physicians who weigh the risks and benefits of more fluid therapy. Encounters are often with unfamiliar patients and evaluation and decisions are completed quickly without involving other physicians. Dynamic testing for fluid responsiveness is used much less often than static methods and fluid boluses are often ordered with no testing at all. This approach is rationalized by factors that discourage dynamic testing: unavailability of equipment, time to obtain test results, or lack of expertise in obtaining valid data. Two mental calculations are particularly influential: physicians’ estimate of the base rate of fluid responsiveness (determined by physical examination, chart review, and previous responses to fluid boluses) and physicians’ perception of patient harm if 500 or 1,000 mL fluid boluses are ordered. When the perception of harm is low, physicians use heuristics that rationalize skipping dynamic testing. LIMITATIONS: Geographic limitation to hospitals in Minnesota, United States. CONCLUSIONS: If dynamic responsiveness testing is to be used more often in routine clinical practice, physicians must be more convinced of the benefits of dynamic testing, that they can obtain valid results quickly and believe that even small fluid boluses harm their patients.

IMPORTANCE: Provider staffing models for ICUs are generally based on pragmatic necessities and historical norms at individual institutions. A better understanding of the role that provider staffing models play in determining patient outcomes and optimizing use of ICU resources is needed. OBJECTIVES: To explore the impact of transitioning from a low- to high-intensity intensivist staffing model on patient outcomes and unit composition. DESIGN, SETTING, AND PARTICIPANTS: This was a prospective observational before-and-after study of adult ICU patients admitted to a single community hospital ICU before (October 2016–May 2017) and after (June 2017–November 2017) the transition to a high-intensity ICU staffing model. MAIN OUTCOMES AND MEASURES: The primary outcome was 30-day all-cause mortality. Secondary outcomes included in-hospital mortality, ICU length of stay (LOS), and unit composition characteristics including type (e.g., medical, surgical) and purpose (ICU-specific intervention vs close monitoring only) of admission. RESULTS: For the primary outcome, 1,219 subjects were included (779 low-intensity, 440 high-intensity). In multivariable analysis, the transition to a high-intensity staffing model was not associated with a decrease in 30-day (odds ratio [OR], 0.90; 95% CI, 0.61–1.34; p = 0.62) or in-hospital (OR, 0.89; 95% CI, 0.57–1.38; p = 0.60) mortality, nor ICU LOS. However, the proportion of patients admitted to the ICU without an ICU-specific need did decrease under the high-intensity staffing model (27.2% low-intensity to 17.5% high-intensity; p < 0.001). CONCLUSIONS AND RELEVANCE: Multivariable analysis showed no association between transition to a high-intensity ICU staffing model and mortality or LOS outcomes; however, the proportion of patients admitted without an ICU-specific need decreased under the high-intensity model. Further research is needed to determine whether a high-intensity staffing model may lead to more efficient ICU bed usage.