ObjectivesThis study investigates risk of mortality associated with nurses’ assessments of patients by physiological system. We hypothesise that nursing assessments of in-patients performed at entry correlate with in-hospital mortality, and those performed just before discharge correlate with postdischarge mortality.DesignCohort study of in-hospital and postdischarge mortality of patients over two 1-year periods.SettingAn 805-bed community hospital in Sarasota, Florida, USA.Subjects42 302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.Outcome measuresAll-cause mortalities and mortality OR.ResultsPatients whose entry nursing assessments, other than pain, did not meet minimum standards had significantly higher in-hospital mortality than patients meeting minimums; and final nursing assessments before discharge had large OR for postdischarge mortality. In-hospital mortality OR were found to be: food, 7.0; neurological, 9.4; musculoskeletal, 6.9; safety, 5.6; psychosocial, 6.7; respiratory, 8.1; skin, 5.2; genitourinary, 3.0; gastrointestinal, 2.3; peripheral-vascular, 3.9; cardiac, 2.8; and pain, 1.1. CI at 95% are within ±20% of these values, with p<0.001 (except for pain). Similar results applied to postdischarge mortality. All results were comparable across the two 1-year periods, with 0.85 intraclass correlation coefficient.ConclusionsNursing assessments are strongly correlated with in-hospital and postdischarge mortality. No multivariate analysis has yet been performed, and will be the subject of a future study, thus there may be confounding factors. Nonetheless, we conclude that these assessments are clinically meaningful and valid. Nursing assessment data, which are currently unused, may allow physicians to improve patient care. The mortality OR and the dynamic nature of nursing assessments suggest that nursing assessments are sensitive indicators of a patient's condition. While these conclusions must remain qualified, pending future multivariate analyses, nursing assessment data ought to be incorporated in risk-related health research, and changes in record-keeping software are needed to make this information more accessible.

ObjectiveTo explore the hypothesis that placing clinical variables of differing metrics on a common linear scale of all-cause postdischarge mortality provides risk functions that are directly correlated with in-hospital mortality risk.DesignModelling study.SettingAn 805-bed community hospital in the southeastern USA.Participants42302 inpatients admitted for any reason, excluding obstetrics, paediatric and psychiatric patients.Outcome measuresAll-cause in-hospital and postdischarge mortalities, and associated correlations.ResultsPearson correlation coefficients comparing in-hospital risks with postdischarge risks for creatinine, heart rate and a set of 12 nursing assessments are 0.920, 0.922 and 0.892, respectively. Correlation between postdischarge risk heart rate and the Modified Early Warning System (MEWS) component for heart rate is 0.855. The minimal excess risk values for creatinine and heart rate roughly correspond to the normal reference ranges. We also provide the risks for values outside that range, independent of expert opinion or a regression model. By summing risk functions, a first-approximation patient risk score is created, which correctly ranks 6 discharge categories by average mortality with p<0.001 for differences in category means, and Tukey's Honestly Significant Difference Test confirmed that the means were all different at the 95% confidence level.ConclusionsQuantitative or categorical clinical variables can be transformed into risk functions that correlate well with in-hospital risk. This methodology provides an empirical way to assess inpatient risk from data available in the Electronic Health Record. With just the variables in this paper, we achieve a risk score that correlates with discharge disposition. This is the first step towards creation of a universal measure of patient condition that reflects a generally applicable set of health-related risks. More importantly, we believe that our approach opens the door to a way of exploring and resolving many issues in patient assessment.

Any clinical laboratory test can be transformed into a mortality odds ratio function, associating mortality risk with each value of the analyte. This provides a DL determined by mortality risk, instead of RI assumptions about distribution in a "healthy" population. The odds ratio function also provides important risk information for analyte values outside the interval.