In SSA, routine vital signs are among the feasible options to monitor treatment of
critically ill patients. Among patients hospitalized with sepsis in Uganda, we used
restricted cubic splines and plotted smoothed curves of admission vital signs data
against the probability of in-hospital death. Using this information, we determined
rules to create a composite prognostic index incorporating vital signs data. Our findings
suggest that prognostic indexes may include vital signs data with reduced segmentation
in the values of included variables, and that low values of respiratory and pulse
rates may not be scored. A proposed prognostic index based on these rules and following
a similar principle as MEWS 18] but with less segmentation adequately predicted mortality and compared favorably
to MEWS.
Prognostic indexes using vital signs data usually contain substantial segmentation
in the values of incorporated variables. For example, MEWS gives: 3 points for each
of SBP 70 mmHg, pulse rate ?130 beats/minute, respiratory rate ?30 cycles/minute,
and GCS score ?8; 2 points for each of SBP 70 to 80 or ?200 mmHg, pulse 111 to 129
beats/minute, respiratory rate 9 or 21 to 29 cycles/minute, temperature 35 or ?38.5°C,
and GCS 9 to 13; 1 point each for SBP 80 to 100 mmHg, pulse rate 40 to 50 or 101 to
110 beats/minute, respiratory rate 16 to 20 cycles/minute, and GCS score of 14; and
0 for all other values 18]. A recent modification suggested for use in SSA includes more variables (seven in
total) and more segmentation in some variables 17]. The high degree of segmentation may affect the efficiency and reliability of these
indexes 27] and, in routine clinical settings, their applicability.
Our data suggest that less segmentation may be achieved in at least four vital signs.
In blood pressure (BP) and temperature, average mortality on either side of the reference
categories did not differ substantially. Although extreme values may be associated
with higher mortality than intermediate values, the data in the extreme ranges are
also likely to be thin most of the time. For respiratory rate and pulse, mortality
did not go above average until respiratory rate ?30 cycles/minute and pulse rate ?100
beats/minute respectively. In addition, low respiratory rates were rare (in both cohorts,
no patient had respiratory rate 12 cycles/minute). We interpret these patterns to
mean that in composite prognostic indexes using vital signs data, low and high values
of BP and temperature may be scored similarly, and that for pulse and respiratory
rate, scoring only the high values may be adequate.
Many prognostic scoring systems have been developed for monitoring critically-ill
patients. These include single-point-in-time-instruments like the Acute Physiology
and Chronic Evaluation (APACHE) 28] and progressive instruments like the Sequential Organ Failure Assessment (SOFA) 29]. However, these tools remain less applicable in acute care settings, where patients
spend limited time. They are even less applicable in resource-limited settings, where
support systems like laboratories are weak. Scoring systems like MEWS that use simple,
yet treatment-modifiable variables, are therefore a welcome addition. However, there
are not enough data supporting their use in both triage and monitoring. Our results
represent one approach to creating newer/simpler triage/monitoring systems.
Monitoring vital signs in acutely ill patients can also aid integrated management
of acute and chronic illnesses. For example, in our data, patients with high BP at
admission had higher mortality than those with normal BP. As hypertension in sepsis
is not usually associated with high mortality 30], this finding may suggest treatment opportunities that could be addressed by better
vital signs monitoring, for example being careful with fluid administration in patients
with pre-existing hypertension, while still giving appropriate fluid resuscitation
to those who may have elevated BP at admission but without pre-existing hypertension.
Our findings suggest that studies using vital signs data to monitor outcomes should
consider modeling these data more flexibly, yet efficiently. Previous studies have
reported relationships between BP and mortality in binary fashion, for example mortality
at MAP 60 mmHg versus ?60 mmHg, or in incremental categories, for example mortality
per 10 unit increase in BP starting from a reference category 8],31]. We suggest that future studies consider the approximately U-shaped patterns in the
relationships of mortality with both BP and temperature and the roughly linear patterns
for respiratory and pulse rates. These patterns should be considered when measuring
vital signs at one point in time, for example at admission, or progressively, for
example at post-admission intervals 32].
Our findings have some limitations. As vital signs were infrequently measured, we
were not able to use multiple measurements for each vital sign, in addition to imputing
some missing values in the development cohort, which may affect the precision of our
measurements. Restricted cubic splines are a flexible way of assessing relationships.
However, the resulting curves are smoothed, and, especially at the extremes, where
data are usually thin, the predictions can be unreliable. As the sample sizes were
small, the OR estimates, especially in the validation cohort, had wide 95% CIs and,
for the estimates assessing the performance of MEWS, a P value not reaching statistical significance. Also, binary modeling, which gives an
equal score at all GCS levels, may lead to loss of information. Future studies should
examine more efficient, yet still simple, ways of modeling not only GCS, but also
other vital signs. Despite these limitations, our findings have potential to guide
the development of prognostic indexes incorporating vital signs data that can be used
to monitor treatment. We included in the proposed index only treatment-modifiable
variables, and, despite the small sample sizes, prediction patterns in the development
cohort were replicated in the validation cohort.