Predictive Rule for Mortality of Inpatients With Escherichia coli Bacteremia: Chi-Square Automatic Interaction Detector Decision Tree Analysis Model.

AIM: A predictive rule for risk factors for mortality due to Escherichia coli (E. coli)bacteremia has not been defined, especially using the chi-square automatic interaction detector (CHAID) decision tree analysis. Here we aimed to create the predictive rule for risk factors for in-hospital mortality due to E. coli bacteremia. METHODS: The outcome of this retrospective cross-sectional survey was death in the hospital due to E. coli bacteremia. Factors potentially predictive of death in the hospital due to E. coli bacteremia were analyzed using the CHAID decision tree analysis. RESULTS: A total of 420 patients (male:female=196:224; mean±standard deviation [SD] age, 75.81±13.13 years) were included in this study. 56 patients (13.3%) died in the hospital. The CHAID decision tree analysis revealed that patients with total protein level ≤5.10 g/dL (incidence, 46.2%), total protein level ≤5.90 g/dL with disturbance of consciousness (incidence, 39.4%), and total protein level >5.90 g/dL with hemoglobin level ≤11.10 g/dL and lactate dehydrogenase level ≥312.0 IU/L (incidence, 42.3%) were included in the high-risk group. CONCLUSIONS: Appropriate preventative therapy should be facilitated in patients with E. coliat a high risk of mortality.

Clinical prediction rule for bacterial arthritis: Chi-squared automatic interaction detector decision tree analysis model.

Objectives: Differences in demographic factors, symptoms, and laboratory data between bacterial and non-bacterial arthritis have not been defined. We aimed to identify predictors of bacterial arthritis, excluding synovial testing. Methods: This retrospective cross-sectional survey was performed at a university hospital. All patients included received arthrocentesis from January 1, 2010, to December 31, 2020. Clinical information was gathered from medical charts from the time of synovial fluid sample collection. Factors potentially predictive of bacterial arthritis were analyzed using the Student's t-test or chi-squared test, and the chi-squared automatic interaction detector decision tree analysis. The resulting subgroups were divided into three groups according to the risk of bacterial arthritis: low-risk, intermediate-risk, or high-risk groups. Results: A total of 460 patients (male/female = 229/231; mean ± standard deviation age, 70.26 ± 17.66 years) were included, of whom 68 patients (14.8%) had bacterial arthritis. The chi-squared automatic interaction detector decision tree analysis revealed that patients with C-reactive protein > 21.09 mg/dL (incidence of septic arthritis: 48.7%) and C-reactive protein ⩽ 21.09 mg/dL plus 27.70 < platelet count ⩽ 30.70 × 104/µL (incidence: 36.1%) were high-risk groups. Conclusions: Our results emphasize that patients categorized as high risk of bacterial arthritis, and appropriate treatment could be initiated as soon as possible.

Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model.

OBJECTIVE: This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. METHODS: This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients' medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student's t-test or the chi-square test and the CHAID decision tree analysis model. RESULTS: In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21,000/µL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21,000/µL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). CONCLUSION: The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.

Missed intracranial injuries are rare in emergency departments using the PECARN head injury decision rules.

PURPOSE: The PECARN head trauma (HT) prediction rules have been developed to guide computed tomography-related decision-making for children with minor HT (mHT). There are currently limited data on the rate of unscheduled revisits to emergency departments (EDs), and initially missed intracranial injuries, in children with mHT initially assessed using the PECARN rules. This study aimed to fill this gap in knowledge. METHODS: Clinical charts of children assessed for mHT over a 5-year period at two EDs that implemented the PECARN rules in Italy and France were reviewed retrospectively. Children who returned to EDs for mHT-related, or potentially related complaints, within 1 month of initial assessment were included. RESULTS: The total number of children with mHT presenting for the first time to the EDs of both sites was 11,749. Overall, 180 (1.5%) unscheduled revisits to the EDs occurred for mHT-related or potentially related complaints. Twenty-three of these 180 patients underwent neuroimaging, and seven had an intracranial injury (including one ischemic stroke). Of these, three were hospitalized and none needed neurosurgery or intensive care. CONCLUSION: Unscheduled revisits for mHT in EDs using the PECARN rules were very uncommon. Initially missed intracranial injuries were rare, and none needed neurosurgery or intensive care.

Derivation and validation of a clinical prediction rule for sleep apnoea syndrome for use in primary care.

BACKGROUND: Several clinical prediction rules (CPRs) are available for sleep apnoea-hypopnoea syndrome (OSAH), but they are difficult to apply in primary care (PC). AIM: Derivation and validation of a CPR using simple measurements available in PC. DESIGN & SETTING: A prospective study conducted in health centres from the area of influence of three Spanish hospitals. METHOD: Patients (aged 18-70 years) who attended for any reason; who presented with at least one of the three key symptoms for OSAH (snoring, breathing pauses while sleeping, and daytime sleepiness); and who were not undergoing non-invasive ventilation or prior treatment with continuous positive airway pressure (CPAP) were included. Anthropometric data, smoking habit, comorbidities, and Epworth test were collected. Patients were subsequently referred to the sleep unit (SU), where the decision was taken whether or not to instigate treatment. A multivariate logistic model was constructed using a sub-sample and scores assigned based on the regression coefficients; the CPR was validated with the remaining sample. Both receiver operating characteristic (ROC) curves were plotted and the sensitivity, specificity, and predictive values calculated. RESULTS: The derivation sample comprised 352 patients, with 260 in the validation sample. The final factors (arterial hypertension [AHT], age, body mass index [BMI], and sex) were used to develop a rule with scores ranging from 0.00-5.50. The cut-off point that optimises the area under the curve (AUC) is ≥2.50 points (AUC = 0.78; sensitivity = 86%; specificity = 54%; positive predictive value [PPV] = 45%; negative predictive value [NPV] = 90%; likelihood ratio [LR] = 0.26). The properties for the validation sample with this cut-off point are as follows: AUC = 0.68; sensitivity = 81%; specificity = 43%; PPV = 61%; NPV = 68%; LR = 0.44. CONCLUSION: As in similar cases, the specificity is low, meaning that healthy people are referred to a specialist. A negative result rules out the disease in most cases.

Use of a self-reported psychosocial distress screening tool as a predictor of need for psychosocial intervention in a general medical setting.

This study describes the development of a self-reported psychosocial distress screening tool for a general medical population and criteria to predict the need for psychosocial intervention. The objectives were to develop criteria to determine which patients need in-person screening and establish criteria identifying patients who are more likely to require psychosocial interventions. The outcomes have bearing on reducing initial psychosocial screening workload for medical social workers in high volume medical settings. Furthermore, a criterion for scoring the self-reported tool can predict which patients will need further social work intervention. The results suggest criteria are a score on the adapted Distress Thermometer of five or greater, at least two negative emotions, and a lack of health insurance. The optimal criterion identified 36% (446/1228) of patients in need of in-person screening with the remaining 64% (782/1228) screened low risk through the tool, representing a significant workload reduction.

A predictive rule for mortality of inpatients with Staphylococcus aureus bacteraemia: A classification and regression tree analysis.

OBJECTIVE: To create a predictive rule to identify risk factors for mortality among patients with Staphylococcus aureus bacteraemia (SAB). DESIGN, SETTING AND PATIENTS: This was a retrospective cohort study of all adult patients with SAB at a large community hospital in Tokyo, Japan, from April 1, 2004 to March 31, 2011. Baseline data and clinically relevant factors were collected from electronic charts. The primary outcome was in-hospital mortality. All candidate predictors were included in a classification and regression tree (CART) analysis. A receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was obtained. A cross-validation analysis was performed. MEASUREMENTS AND MAIN RESULTS: A total of 340 patients had SAB during the study period. Of these, 118 (34.7%) patients died in hospital. Among 41 potential variables, the CART analysis revealed that underlying malignancy, serum blood glucose level, methicillin resistance, and low serum albumin were predictors of mortality. The AUC was 0.73 (95% CI: 0.67-0.79). For validation, the estimated risk was 0.26 (± SE: 0.02) in the resubstitution analysis and 0.33 (± SE: 0.03) in the cross-validation analysis. CONCLUSION: We propose a predictive model for the mortality of patients with SAB consisting of four predictors: underlying malignancy, low serum albumin, high glucose, and methicillin resistance. This model may facilitate appropriate preventative management for patients with SAB who are at high risk of mortality.