title: The relationship between in-hospital mortality, readmission into the intensive care nursing unit and/or operating theatre and nurse staffing levels.
abstract: OBJECTIVE
  The aim of this article was to assess the relationship between (1) in-hospital mortality and/or (2) unplanned readmission to intensive care units or operating theatre and nurse staffing variables.
  
  
  BACKGROUND
  Adverse events are used as surrogates for patient safety in nurse staffing and patient safety research. A single adverse event cannot adequately capture the multi-dimensional attributes of patient safety; hence, there is a need to consider composite measures. Unplanned readmission into the postoperative Intensive Care nursing unit and/or operating Theatre and in-hospital mortality can be viewed as measures that incorporate the effects of several adverse events.
  
  
  METHODS
  We conducted a Bayesian multilevel analysis on a subset of the 2003 Belgian Hospital Discharge and Nursing Minimum Data sets. The sample included 9054 patients who underwent coronary artery bypass surgery or heart valve procedures from 28 Belgian acute hospitals. Two proxies of patient safety were considered, namely postoperative in-hospital mortality in the first postoperative intensive care unit and unplanned readmission into the intensive care and/or operating theatre (including mortality beyond the first postoperative intensive care unit) after the first-operative intensive care nursing unit.
  
  
  RESULTS
  There is an association between in-hospital mortality and/or unplanned readmissions and nurse staffing levels, but the relationship is moderated by volume and severity of illness respectively. In addition, the relationship differs between the two endpoints.
  
  
  CONCLUSIONS
  Higher nurse staffing levels on postoperative general nursing cardiac surgery units protected patients from unplanned readmission to intensive care units or operating theatre and in-hospital mortality.
authors: Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, Belgium. luwis.diya@med.kuleuven.be
authors: Luwis
authors: L
authors: Diya
authors: Koen
authors: K
authors: Van den Heede
authors: Walter
authors: W
authors: Sermeus
authors: Emmanuel
authors: E
authors: Lesaffre
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001530:Belgium; D006348:Cardiac Surgical Procedures / Q000401:mortality* / Q000451:nursing; D003430:Cross-Sectional Studies; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009741:Nursing Staff, Hospital / Q000193:education / Q000706:statistics & numerical data / Q000600:supply & distribution*; D009873:Operating Rooms / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D061214:Patient Safety / Q000706:statistics & numerical data; D011182:Postoperative Care / Q000451:nursing; D012189:Retrospective Studies; D012720:Severity of Illness Index; D000078329:Workforce; D055815:Young Adult