Development of a simple model for predicting need for surgery in patients who initially undergo conservative management for adhesive small bowel obstruction.

BACKGROUND: Among patients with adhesive small bowel obstruction (ASBO) initially managed with a conservative strategy, predicting risk of operation is difficult. METHODS: We investigated ASBO patients at 2 different periods to derive and validate a clinical prediction model for risk of operation. RESULTS: One hundred fifty-four patients were enrolled into the derivation cohort and 96 into the validation cohort. Based on the derived scoring, including age > or =65 years, presence of ascites, and gastrointestinal drainage volume >500 mL on day 3, each patient was classified into 1 of 4 risk classes from low risk to high risk. When applied to the validation cohort, the positive predictive value (PPV) for operation in the high-risk class was 72%, while the negative predictive value (NPV) in the low-risk class was 100% with high sensitivity (100%) and specificity (96%). CONCLUSIONS: The prediction model performs well for risk stratification of need for surgical intervention following conservative strategy among ASBO patients.

Chronic hepatitis with eosinophilic infiltration associated with asthma.

A 49-year-old asthmatic woman with a history of drug allergy to aspirin and penicillin presented to the hospital with fever, dark urine, general exanthema and fatigue. Physical examination revealed jaundice. She had been taking four different over-the-counter drugs. Laboratory examination showed eosinophilia, elevated Ig E, hyperbilirubinemia and elevated liver function tests. HBs Ag, anti-HCV Ab and Ig M anti-HA were negative. She was initially diagnosed with drug-induced hepatitis and was treated with steroids. However, liver dysfunction with eosinophilia relapsed after discontinuing steroids. Liver biopsy revealed eosinophilic infiltration. Steroids were discontinued after the start of 6-mercaptopurine.

Increasing vigilance on the medical/surgical floor to improve patient safety.

AIM: This paper reports a study designed to assess an automated non-invasive, patient vigilance system, the (L)G(1TM) system, for determining heart rate and respiration rate. The study uses collected data to optimize the (L)G(1TM)'s alert management scheme for medical/surgical wards. BACKGROUND: Thousands of patients die unnecessarily each year because of compromised patient safety in hospitals. Economic pressures to reduce hospitalization costs, exacerbated by increasing nursing shortages, have created a need for new approaches to patient vigilance. Advanced technologies may help nurses to provide high-quality care while controlling costs and improving patient safety. METHODS: Heart and respiration waveforms from 287 patients were captured by sensor arrays embedded in the mattress coverlets of their beds. No real-time monitoring was performed. Raw data were processed by proprietary algorithms and compared with data captured by a standard reference device. Alert performance was verified by hand-scoring the signal data and matching it against clinical events observed through a systematic review of each patient's medical record. The data were collected between June 2004 and February 2005. RESULTS: Experimental algorithms for heart rate had an accuracy of -1.47 (sd 1.90) and a precision of 4.60 (sd 2.46). Respiration rate algorithms showed an accuracy of -0.94 (sd 1.26) and a precision of 4.02 (sd 1.17). Algorithms identified 178 true-positive physiological alerts on 15 patients. None of the events was deemed clinically significant at chart review. The combined false-positive alert rate for the algorithms was 0.007 events per hour. CONCLUSION: This study demonstrates the accuracy and precision of the signal processing algorithms in the (L)G(1TM) system. Future work will focus on assessing the system's impact on patient outcomes and its integration into the nursing workflow.

Prevalence and risk factors associated with hepatitis C in ED patients.

The objective of this study was to measure the prevalence of and identify risk factors associated with hepatitis C virus (HCV) in emergency department (ED) patients. Adults presenting to an urban university teaching hospital, having blood drawn as part of their routine evaluation, had an extra tube drawn and tested for HCV. English-speaking adults consenting to participate in the survey portion of the study were administered an in-depth risk factor questionnaire. A case-control analysis was used to identify risk factors in HCV+ compared with HCV- subjects. Contingency table analyses using the Fisher exact test, odds ratios (OR), and 95% confidence intervals (CI) were used for risk factor identification. Of 223 blood samples, 38 (17%) were positive for antibodies for HCV. One hundred twenty-one subjects (54%) agreed to the risk factor survey, 18 (15%) of whom were HCV+. Of the 18 HCV+ survey participants, 12 new diagnoses of HCV were made. A history of injection drug use was the most significant risk factor associated with HCV (OR 858.5, CI 61.8-22,026.5). A high prevalence of HCV is found in selected urban ED patients. Most of these patients have a constellation of risk factors including a history of injection drug use. Efforts to identify at risk patients for serologic testing and follow-up should be initiated. Identifying undiagnosed HCV can lead to interventions to decrease transmission as well as reduce the morbidity and mortality of disease.