title: Nighttime intensivist staffing, mortality, and limits on life support: a retrospective cohort study.
abstract: BACKGROUND
  Evidence regarding nighttime physician staffing of ICUs is suboptimal. We aimed to determine how nighttime physician staffing models influence patient outcomes.
  
  
  METHODS
  We performed a multicenter retrospective cohort study in a multicenter registry of US ICUs. The exposure variable was the ICU's nighttime physician staffing model. The primary outcome was hospital mortality. Secondary outcomes included new limitations on life support, ICU length of stay, hospital length of stay, and duration of mechanical ventilation. Daytime physician staffing was studied as a potential effect modifier.
  
  
  RESULTS
  The study included 270,742 patients in 143 ICUs. Compared with nighttime staffing with an attending intensivist, nighttime staffing without an attending intensivist was not associated with hospital mortality (OR, 1.03; 95% CI, 0.92-1.15; P = .65). This relationship was not modified by daytime physician staffing (interaction P = .19). When nighttime staffing was subcategorized, neither attending nonintensivist nor physician trainee staffing was associated with hospital mortality compared with attending intensivist staffing. However, nighttime staffing without any physician was associated with reduced odds of hospital mortality (OR, 0.79; 95% CI, 0.68-0.91; P = .002) and new limitations on life support (OR, 0.83; 95% CI, 0.75-0.93; P = .001). Nighttime staffing was not associated with ICU or hospital length of stay. Nighttime staffing with an attending nonintensivist was associated with a slightly longer duration of mechanical ventilation (hazard ratio, 1.05; 95% CI, 1.02-1.09; P < .001).
  
  
  CONCLUSIONS
  We found little evidence that nighttime physician staffing models affect patient outcomes. ICUs without physicians at night may exhibit reduced hospital mortality that is possibly attributable to differences in end-of-life care practices.
authors.affiliation: Pulmonary, Allergy, and Critical Care Division, Department of Medicine; Center for Clinical Epidemiology and Biostatistics, Leonard Davis Institute of Health Economics.
authors.forename: Meeta Prasad
authors.initials: MP
authors.lastname: Kerlin
authors.affiliation: Center for Clinical Epidemiology and Biostatistics, Leonard Davis Institute of Health Economics.
authors.forename: Michael O
authors.initials: MO
authors.lastname: Harhay
authors.affiliation: Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Department of Critical Care, University of Pittsburgh School of Medicine, Pittsburgh, PA.
authors.forename: Jeremy M
authors.initials: JM
authors.lastname: Kahn
authors.affiliation: Pulmonary, Allergy, and Critical Care Division, Department of Medicine; Center for Clinical Epidemiology and Biostatistics, Leonard Davis Institute of Health Economics; Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, P30 Roybal Center on Behavioral Economics and Health and Fostering Improvement in End-of-Life Decision Science (FIELDS) Program, University of Pennsylvania, Philadelphia, PA.
authors.forename: Scott D
authors.initials: SD
authors.lastname: Halpern
mesh_terms: D000368:Aged; D016638:Critical Illness / Q000401:mortality* / Q000628:therapy; D005260:Female; D005500:Follow-Up Studies; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007362:Intensive Care Units; D008020:Life Support Care / Q000458:organization & administration*; D008297:Male; D008875:Middle Aged; D010561:Personnel Staffing and Scheduling; D011787:Quality of Health Care; D012042:Registries; D012189:Retrospective Studies; D012307:Risk Factors; D015996:Survival Rate / Q000639:trends; D014481:United States / Q000453:epidemiology; D000078329:Workforce