Objective To explore and establish a more precise and reasonable classification method which is suitable for clinical treatment and scientific research of SARS patients. Methods ① Establishing a computerized classification method: Analyzing the relationship between variable items on the front page of medical records and severity of disease; Identifying the variable items related to patient’s condition by stepwise identification analysis; Creating a function equation and computerized classification system. ② Comparing and analyzing the difference between computerized and clinical classifications regarding to the general condition of patients, clinical manifestations, laboratorial test results, prognosis, period of hospitalization and medical expenditure, etc. Results ① Clinical classification: general cases 642 (94.41%), critical cases 38 (5.59%); Computerized classification: type A 436 (64.12%), type C 237 (34.85%), type D 7 (1.03%), no type B. ② There were statistical significance among groups between two classifications regarding the items of general condition (age, cure rate, mortality and average length of hospitalization), total protein , Alb, BUN and medical expenditure. ③ Comparative analysis of the two classifications: 99.77% of type A cases (general type) by computerized classification were general cases by clinical classification; 97.36% of critical cases by clinical classification were type Camp;D by computerized classification. Conclusions The results are conformity between two classifications and the differences are analogical among thegroups. The statistical difference is significant between general and critical cases with the number of critical cases by computerized classification 6.42 times more than that by clinical classification; Compared with clinical classification, computerized classification has advantages that there is significant difference between the groups while no difference within the groups. With more critical cases and more objective and logic results, the compauterized classification is suitable for study and application in the fields of health service quality management, health economy management and pharmaceutical economics, etc.
Objective This study analyzed the medical expenditure and its influential factors, and compared the clinical effectiveness and medical expenditure of three major drugs. Methods We designed the cohort study to compare the difference of medical and pharmaceutical expenditures between patients with and without underlying diseases. Multi-linear regression was applied to analyze the influential factors. Incremental expenditure-effectiveness ratio was applied to study three clinically important drugs. Results The curing rate of non-critical patients was statistically significant than critical patients (73.68%, 99.38%, P=0.000) .The curing rate of non-critical patients without underlying diseases was statistically significant than those with underlying diseases in the cohort (96%, 99.66%, P=0.001 6). No significance was identified in the critical patients cohort. The medical expenditure of non-critical patients with and without underlying diseases were 7 879.22 and 7 172.23 RMB per capita, respectively. Accordingly, the medical expenditure in critical patients was 24 912.89 and 26 433.53 RMB per capita. No significance was identified in the two cohorts. Medical expenditure was positively correlated with age and disease severity, with its equation y=4585.71+79.04X1+17188.87X2 (X1: age, X2: disease severity). Regarding the clinical effectiveness and medical expenditure, no significance was identified in critical patients who administered small and medium dose of Methylprednisolone. The expenditure-effectiveness ratios of Ribavirin that was administered by non-critical patients without underlying dissuades were 6 107 and 4 225 RMB, respectively. Accordingly, the expenditure-effectiveness ratios of Thymosin were 11 651 and 6 107 RMB. Conclusions The curing rate of non-critical patients without underlying diseases was higher than the counterpart in the cohort. No influence of underlying diseases was found in the critical patient cohort. Medical expenditure was positively correlated with age and disease severity. Small-and-medium dose of Methylprednisolone might not influence the curing rate and medical expenditure in critical patients. The effectiveness of Thymosin for non-critical patients with and without underlying diseases was not significantly different. However, additional 5 877 RMB occurred if Thymosin was administrated. Likewise, the effectiveness of Ribavirin for non-critical patients remains the same. However, additional 1 082 RMB was consumed in Ribavirin-administrated patient.
Objective To summarize primary clinical data from Xiao Tang Shan Hospital (XTSH) Information System, to provide evidence for clinical data of emerging diseases. Method The primary data were extracted from XTSH information system, which related to demographic and background information, case history, prescriptions, laboratory tests, physical examination, vital sign, surgery, diagnostics and expenditures. The software for data verification was developed by Delphi language program. The information of SARS management was developed by Oracle Developer. Results XTSH information system for SARS management collected 1.09 million pieces of information covering 680 SARS cases. The database was functionally divided into inquiry window, conditional case list window and case details spread window, which provided information of SARS management and shaped a platform for further investigation. Quality control of clinical data was done by the software of SARS Information Real Control.Conclusions XTSH information system collected complete data of SARS management, which made healthcare, research and policy-making on SARS accessible, and made it possible to share resources and train the professionals.
Objective To investigate the adverse drug reactions (ADRs) of patients with SARS in Xiao Tang Shan Hospital. Methods We developed and distributed Drug Use Handbook and established ADRs monitoring group to guide resaonable drug use. We followed up the process and collected clinical report on ADRs. We retrospectively analyzed the data on ADRs by the classification and grade of ADRs according to WHO and Hospital Information Sysytem (HIS) of Chinese PLA General Hospital. Results We collected 193 (87 males and 106 females) patients with ADRs among 680 SARS patients with incidence rate of ADRs of 28.38%. The ADRs incidence rate was higher in females and elders. Critical SARS patients and SARS patients with diabetes were more susceptible to ADRs. Large dosage and combination of drugs may induce ADRs. Steroids may be a main cause of ADRs. The ADRs incidence rate induced by injection was higher than that induced by all kinds of oral drugs. ADRs mainly happened in hematological, endocrine and digestive systems. Conclusion SARS patients are prescribed many kinds of medications. Large dosage of so many medications may lend to high incidence rate of ADRs. Steroid should be cautiously used in the treatment of SARS.
Objective To study the medication usage in patients with severe acute respiratory syndrome (SARS). Methods The information of the medications of 680 patients with SARS in Xiao Tang Shan Hospital was collected by HIS system and classified by using computer model on the basis of disease factor. The usage time and cases, and cost of these medications were calculated. The defined daily dose (DDD) and drug utilization index (DUI) were analyzed. Results A number of 359 drugs in 17 categories were applied to 680 patients with SARS. Most cases used antibacterial agents, the DDDs of immunomodulator and vitamins were the highest, the usage duration of vitamins and infusion fluids were the longest among 17 kinds of the drugs. The cost of methylpredni-solone injection was the highest. The mortality rate, kinds of drugs and frequency of drugs were higher in type C, type D and serious SARS patients than that of type A and common SARS patients. Conclusions Many kinds of medications have been prescribed to SARS patients. The dosages of these medication are very high, especially glucocorticoids, immuneomodulator and nutrient agents. The computer model on the basis of disease factor is probably valid, rapid and easy to standardize.