CAN PLOTTING CLINICAL TREATMENT RESPONSE RATES AND CALCULATING DISEASE SLOPES OVER TIME IMPROVE MEDICAL DECISION MAKING?

Purpose:    Our goals were to determine if patient severity of illness trends over time could be plotted to show differences in response to treatment; and if early severity changes predicted outcomes.  We hypothesize that medical decision making may improve as clinicians compare quantitative summaries of patient trends with similar historic patients.

Method:    This is a secondary analysis of an existing retrospective cohort of 1220 hospitalized adults randomly selected over one year.  Every 2 hours after treatment began in the ED, vital signs were converted to partial APACHE III Scores, and entered along with development of nosocomial infections (NI) and mortality.  Mean temporal changes were calculated for important subgroups.

Result: Of 1220 patients, 1037 had no NI and lived, 152 developed NI, 42 died, and 183 either died or had NI.  Figure 1 shows the 1220 patients grouped into decile groups and raw scores plotted every two hours. It is interesting to note that the groups all rapidly improved with ED treatment, but relapsed at 8 - 12 hours. Figure 2 displays the dramatically different score changes for the subgroups:  a. Died/No NI; b. Died with NI; c. NI/did not Die; d. No NI/did not Die. 

Conclusion: Our findings suggest that plotting disease trends to display rates of resolution is feasible.  Using EMR databases adding more clinical data such as lab results in a disease-specific trend score, may allow clinicians to compare improvement rates for a current individual with a historic cohort of similar patients.     We postulate that the uniform worsening of patient scores between 8 – 12 hours coincided with starting routine medication intervals in the hospital setting, in contrast to initial ED treatments immediately on arrival. The clear differences in the score changes over time based on patient outcomes need further study.    We envision a new test called “trend” or “slope” specific for DKA, asthma, or cardiogenic shock, to alert clinicians when responses are slow – and when to consider more aggressive treatment, or alternate diagnoses.  The result might be expressed as a change in disease score per hour or day. Treatment timing can also be adjusted to correct the lapses shown on Figure 1. This will be feasible as eMR technology grows.