Derivation and validation of a score based on Hypotension, Oxygen saturation, low Temperature, ECG changes and Loss of independence (HOTEL) that predicts early mortality between 15 min and 24 h after admission to an acute medical unit.

BACKGROUND: Predictive scores such as APACHE II have been used to assess patients in intensive care units, but few scores have been used to assess acutely ill general medical patients. DESIGN: Examination of the ability of clinical variables documented at the time of admission to predict early mortality between 15 min and 24 h after admission. SETTING: An Irish rural hospital. SUBJECTS: 10,290 consecutive patients admitted as acute medical emergencies, divided into a derivation cohort of 6947 patients and a validation cohort of 3343 patients. RESULTS: 40 patients of the derivation cohort (0.6%) died within 24h of hospital admission. Multivariate analysis revealed 11 independent predictors of early death from which a simplified model with minimal loss of predictive ability was derived. Since this model contained only the five variables of Hypotension (systolic blood pressure<100 mm Hg), low Oxygen saturation (<90%), low Temperature (<35 degrees C, abnormal ECG and Loss of independence (unable to stand unaided) it was named the HOTEL score (one point for each variable). There were no differences in the early mortality predicted by this score between the derivation and validation cohorts-the area under the receiver operator characteristic curves for the derivation and validation cohorts were 86.5% and 85.4%, respectively. None of the patients with a score of zero died within 15 min and 24 h and a score of one had an early mortality of 0.3% in both cohorts. A score of two had an early mortality of 0.9% in the derivation cohort and 1.7% in the validation cohort, while a score of three or greater had an early mortality of 10.2% in the derivation and 5.6% the validation cohort. CONCLUSIONS: The HOTEL score quickly identifies patients at a low and high risk of death between 15 min and 24 h after admission, thus enabling prompt triage and placement within a health care facility.

Should age be included as a component of track and trigger systems used to identify sick adult patients?

AIM OF STUDY: Few published "track and trigger systems" used to identify sick adult patients incorporate patient age as a variable. We investigated the relationship between vital signs, patient age and in-hospital mortality and investigated the impact of patient age on the function as predictors of in-hospital mortality of the two most commonly used track and trigger systems. MATERIALS AND METHODS: Using a database of 9987 vital signs datasets, we studied the relationship between admission vital signs and in-hospital mortality for a range of selected vital signs, grouped by patient age. We also used the vital signs data set to study the impact of patient age on the relationship between patient triggers using the "MET criteria" and "MEWS", and in-hospital mortality. RESULTS: At hospital discharge, there were 9152 (91.6%) survivors and 835 (8.4%) non-survivors. As admission vital signs worsened, mortality increased for each age range. Where groups of patients had triggered a certain MET criterion, mortality was higher as patient age increased. Mortality varied significantly with age (p<0.05; Fishers exact test) for breathing rate >36breathsmin(-1), systolic BP<90mmHg and decreased conscious level. For each age group, mortality also increased as total MEWS score increased. As the number of simultaneously occurring MEWS abnormalities, or simultaneously occurring MET criteria, increased, mortality increased for each age range. CONCLUSIONS: Age has a significant impact on in-hospital mortality. Our data suggest that the inclusion of age as a component of these systems could be advantageous in improving their function.

The diagnoses and co-morbidity encountered in the hospital practice of acute internal medicine.

BACKGROUND: The exact medical conditions that every internist needs to know how to diagnose and treat have seldom been explicitly stated. This paper reports an analysis of the conditions, as identified by ICD9 coding, cared for by general internists working in a representative Irish hospital. METHODS: In this observational study covering the period from February 17, 2000 to January 29, 2004, the ICD9 codes and mortality of 9214 consecutive patients admitted as acute medical emergencies were examined. RESULTS: The mean number of ICD9 codes per patient was 4.0+/-1.8 (median 4.0 codes); 935 patients (10.1%) had one ICD9 code and 2972 (32.3%) had six ICD9 codes recorded at the time of discharge. As the number of ICD9 codes recorded increased, so did patient age, 30-day mortality and length of hospital stay. Thirty-four conditions were found to be associated with a statistically significant increased risk of 30-day mortality, and eight with a significantly reduced risk. Of the remaining conditions (i.e. those with neither an increased nor reduced risk of mortality), 32 were observed in 1% or more of all patients. DISCUSSION: Nearly all of the clinical presentations encountered are encompassed within an average of four combinations of 74 conditions, 34 of which are associated with an increased risk of death.

When should a doctor see me when I get sick? A study of the time of day acutely ill medical patients present and the time they wait to see a doctor in Ireland.

BACKGROUND: Reconfiguration of the Irish Health Service has diverted of large numbers of acutely ill medical patients to a reduced number of hospitals and may have caused in delays in treatment. Although prompt care improves outcomes for patients with acute myocardial infarction, stroke, infection and shock, there is surprisingly little evidence for its value in other conditions. METHODS: The time of admission and time patients waited to be seen and clerked by a doctor was reviewed on all medical patients admitted to Nenagh Hospital prior to service reconfiguration (i.e. from 17 February 2000 to 6 March 2004). RESULTS: Over the study period of 1442,days 9435 patients were admitted (i.e. 6.5 patients per day or 0.3 per hour) and waited 37.6 SD 53.1min after admission before they were seen by a doctor. The peak time of admission is in the late afternoon and early evening and there was a liner correlation between the delay before seeing a doctor and the time of admission. The 1095 patients who waited 80min or more to be seen and clerked by a doctor (median delay 120min) were more likely to die (odds ratio 1.36 95% CI 1.03-1.81, p <0.03). CONCLUSION: Waiting to be seen by a doctor may increase the risk of death to some patients. For these patients it is probably safer to be seen quickly by any doctor, rather than travel many miles and wait several hours to see a better one.

Who will be sicker in the morning? Changes in the Simple Clinical Score the day after admission and the subsequent outcomes of acutely ill unselected medical patients.

BACKGROUND: All doctors are haunted by the possibility that a patient they reassured yesterday will return seriously ill tomorrow. We examined changes in the Simple Clinical Score (SCS) the day after admission, factors that might influence these changes and the relationship of these changes to subsequent clinical outcome. METHOD: The SCS was recorded in 1165 patients on admission and again the following day (i.e. 25.0±15.8 h later). The abilities of 51 variables that might predict changes in the SCS were examined. RESULTS: The day after admission 16.1% of patients had been discharged home, 31.4% had decreased their SCS by 2.4±1.6 points, 38.6% had an unchanged SCS, 12.0% had increased their SCS by 2.1±1.7 points and 1.2% had died. Patients with an increased SCS had higher in-hospital mortality (10% vs. 1.1%, OR 10.1, p<.001) and a longer length of stay (9.4±9.6 vs. 5.6±7.0 days, p<.001). There was no consistent association between the SCS recorded at admission and SCS increase. Only nursing home residence, heart failure and a Medical Admission Risk System laboratory data score>0.09 were found to be independent predictors of SCS increase. CONCLUSION: The SCS of 12% of patients increases the day after admission to hospital, which is associated with a ten-fold increase of in-hospital mortality. Low SCS risk patients are just as likely to have a SCS increase as high risk patients.

What diagnoses may make patients more seriously ill than they first appear? Mortality according to the Simple Clinical Score Risk Class at the time of admission compared to the observed mortality of different ICD9 codes identified on death or discharge.

BACKGROUND: The Simple Clinical Score (SCS) determined at the time of admission places acutely ill general medical patients into one of five risk classes associated with an increasing risk of death within 30 days. The cohort of acute medical patient that the SCS was derived from had, on average, four combinations of 74 groupings of ICD9 codes. This paper reports the ICD9 codes associated with the different SCS risk classes and identifies those ICD9 codes with a greater observed mortality than that of other patients in the same SCS risk class. DESIGN: Observational study. SETTING: A small Irish rural hospital. METHODS: The 30-day mortality rates of the 74 commonest ICD9 groupings coded at the time of discharge of 9214 consecutive acutely ill medical patients were compared with the mortality rates associated with their SCS risk class determined at the time of their admission. RESULTS: There was no difference between the observed and the predicted mortality rates for very low risk patients regardless of ICD9 groupings, even though several of these patients suffered from all but two of the 34 ICD9 code groupings associated with an increased risk of death. Within the remaining four risk classes only 14 ICD9 groupings had an observed mortality greater from that of all other patients in the same SCS risk class. CONCLUSION: The Simple Clinical Score (SCS) determined at the time of admission identifies patients at very low risk of death regardless of what diagnoses are subsequently made during their hospitalisation. Nevertheless, patients with a very low risk of death according to their SCS risk class may still have a life-threatening condition that requires treatment in hospital. For higher risk patients only 14 ICD9 code groupings were associated with an observed mortality greater than that of others in the same SCS risk group.