Staphylococcus Intermedius Infections: Case Report and Literature Review

Staphylococcus intermedius is part of the normal skin and oral flora of dogs. Case reports of human infections are rare, but the true incidence is unknown because the pathogen is frequently misidentified as Staphylococcus aureus. Reported cases range from soft tissue infections to brain abscess. Most reported cases in humans have been related to dog exposure. We report a case of a 73 year old female with S. intermedius surgical wound infection one month following a left elbow total arthroplasty. This is the first reported human case of S. intermedius infection of a mechanical prosthesis. The presumed source of infection was the patient’s dog. The patient was treated with vancomycin, then switched to cefazolin and rifampin once susceptibilities were known. Case reports suggest that patients generally respond well to tailored antibiotics with complete or near-complete recovery. S. intermedius should be included in the differential diagnosis of invasive infection amongst patients with close contact with dogs

Interrater reliability of surveillance for ventilator-associated events and pneumonia

OBJECTIVETo compare interrater reliabilities for ventilator-associated event (VAE) surveillance, traditional ventilator-associated pneumonia (VAP) surveillance, and clinical diagnosis of VAP by intensivists.DESIGNA retrospective study nested within a prospective multicenter quality improvement study.SETTINGIntensive care units (ICUs) within 5 hospitals of the Centers for Disease Control and Prevention Epicenters.PATIENTSPatients who underwent mechanical ventilation.METHODSWe selected 150 charts for review, including all VAEs and traditionally defined VAPs identified during the primary study and randomly selected charts of patients without VAEs or VAPs. Each chart was independently reviewed by 2 research assistants (RAs) for VAEs, 2 hospital infection preventionists (IPs) for traditionally defined VAP, and 2 intensivists for any episodes of pulmonary deterioration. We calculated interrater agreement using κ estimates.RESULTSThe 150 selected episodes spanned 2,500 ventilator days. In total, 93–96 VAEs were identified by RAs; 31–49 VAPs were identified by IPs, and 29–35 VAPs were diagnosed by intensivists. Interrater reliability between RAs for VAEs was high (κ, 0.71; 95% CI, 0.59–0.81). Agreement between IPs using traditional VAP criteria was slight (κ, 0.12; 95% CI, −0.05–0.29). Agreement between intensivists was slight regarding episodes of pulmonary deterioration (κ 0.22; 95% CI, 0.05–0.39) and was fair regarding whether episodes of deterioration were attributable to clinically defined VAP (κ, 0.34; 95% CI, 0.17–0.51). The clinical correlation between VAE surveillance and intensivists’ clinical assessments was poor.CONCLUSIONSProspective surveillance using VAE criteria is more reliable than traditional VAP surveillance and clinical VAP diagnosis; the correlation between VAEs and clinically recognized pulmonary deterioration is poor.Infect Control Hosp Epidemiol 2017;38:172–178</jats:sec

Obesity as a risk factor for severe influenza-like illness

Background: Obesity was recognized as in independent risk factor for influenza during the 2009 H1N1 influenza pandemic. Objectives: We evaluated the association between body mass index (BMI) and influenza-like illness (ILI) during two non-pandemic influenza seasons (2003–2004 and 2004–2005) and during the spring and fall waves of the 2009 H1N1 pandemic. Methods: Adults with severe (inpatient) and mild (outpatient) ILI were compared to those without ILI using a case-cohort design. The study was nested among those insured by a single health insurance company, receiving care from a large multispecialty practice. Data were collected from insurance claims and the electronic health record. The primary exposure was obesity (BMI ≥ 30·0 kg/m2). Results: Across three seasons, the crude and adjusted ORs for obesity and severe ILI were 1·65 (95% CI 1·31, 2·08) and 1·23 (95% CI 0·97, 1·57), respectively. An association was observed for those aged 20–59 years (adjusted OR 1·92, 95% CI 1·26, 2·90), but not for those 60 and older (adjusted OR 1·08, 95% CI 0·80, 1·46). The adjusted ORs for obesity and severe ILI in 2003–2004, 2004–2005, and during H1N1 were 1·14 (95% CI 0·80, 1·64), 1·24 (95% CI 0·86, 1·79), and 1·76 (95% CI 0·91, 3·42), respectively. Among those with a Charlson Comorbidity Index score of zero, the adjusted ORs for 2003–2004, 2004–2005, and H1N1 were 1·60 (95% CI 0·93, 2·76), 1·43 (95% CI 0·80, 2·56), and 1·90 (95% CI 0·68, 5·27), respectively. Conclusions: Our results suggest a small to moderate association between obesity and hospitalized ILI among adults

Prevalence, underlying causes, and preventability of sepsis-associated mortality in US acute care hospitals

Importance: Sepsis is present in many hospitalizations that culminate in death. The contribution of sepsis to these deaths, and the extent to which they are preventable, is unknown. Objective: To estimate the prevalence, underlying causes, and preventability of sepsis-associated mortality in acute care hospitals. Design, Setting, and Participants: Cohort study in which a retrospective medical record review was conducted of 568 randomly selected adults admitted to 6 US academic and community hospitals from January 1, 2014, to December 31, 2015, who died in the hospital or were discharged to hospice and not readmitted. Medical records were reviewed from January 1, 2017, to March 31, 2018. Main Outcomes and Measures: Clinicians reviewed cases for sepsis during hospitalization using Sepsis-3 criteria, hospice-qualifying criteria on admission, immediate and underlying causes of death, and suboptimal sepsis-related care such as inappropriate or delayed antibiotics, inadequate source control, or other medical errors. The preventability of each sepsis-associated death was rated on a 6-point Likert scale. Results: The study cohort included 568 patients (289 [50.9%] men; mean [SD] age, 70.5 [16.1] years) who died in the hospital or were discharged to hospice. Sepsis was present in 300 hospitalizations (52.8%; 95% CI, 48.6%-57.0%) and was the immediate cause of death in 198 cases (34.9%; 95% CI, 30.9%-38.9%). The next most common immediate causes of death were progressive cancer (92 [16.2%]) and heart failure (39 [6.9%]). The most common underlying causes of death in patients with sepsis were solid cancer (63 of 300 [21.0%]), chronic heart disease (46 of 300 [15.3%]), hematologic cancer (31 of 300 [10.3%]), dementia (29 of 300 [9.7%]), and chronic lung disease (27 of 300 [9.0%]). Hospice-qualifying conditions were present on admission in 121 of 300 sepsis-associated deaths (40.3%; 95% CI 34.7%-46.1%), most commonly end-stage cancer. Suboptimal care, most commonly delays in antibiotics, was identified in 68 of 300 sepsis-associated deaths (22.7%). However, only 11 sepsis-associated deaths (3.7%) were judged definitely or moderately likely preventable; another 25 sepsis-associated deaths (8.3%) were considered possibly preventable. Conclusions and Relevance: In this cohort from 6 US hospitals, sepsis was the most common immediate cause of death. However, most underlying causes of death were related to severe chronic comorbidities and most sepsis-associated deaths were unlikely to be preventable through better hospital-based care. Further innovations in the prevention and care of underlying conditions may be necessary before a major reduction in sepsis-associated deaths can be achieved

Multicenter Evaluation of a Novel Surveillance Paradigm for Complications of Mechanical Ventilation

Ventilator-associated pneumonia (VAP) surveillance is time consuming, subjective, inaccurate, and inconsistently predicts outcomes. Shifting surveillance from pneumonia in particular to complications in general might circumvent the VAP definition's subjectivity and inaccuracy, facilitate electronic assessment, make interfacility comparisons more meaningful, and encourage broader prevention strategies. We therefore evaluated a novel surveillance paradigm for ventilator-associated complications (VAC) defined by sustained increases in patients' ventilator settings after a period of stable or decreasing support.We assessed 600 mechanically ventilated medical and surgical patients from three hospitals. Each hospital contributed 100 randomly selected patients ventilated 2-7 days and 100 patients ventilated >7 days. All patients were independently assessed for VAP and for VAC. We compared incidence-density, duration of mechanical ventilation, intensive care and hospital lengths of stay, hospital mortality, and time required for surveillance for VAP and for VAC. A subset of patients with VAP and VAC were independently reviewed by a physician to determine possible etiology.Of 597 evaluable patients, 9.3% had VAP (8.8 per 1,000 ventilator days) and 23% had VAC (21.2 per 1,000 ventilator days). Compared to matched controls, both VAP and VAC prolonged days to extubation (5.8, 95% CI 4.2-8.0 and 6.0, 95% CI 5.1-7.1 respectively), days to intensive care discharge (5.7, 95% CI 4.2-7.7 and 5.0, 95% CI 4.1-5.9), and days to hospital discharge (4.7, 95% CI 2.6-7.5 and 3.0, 95% CI 2.1-4.0). VAC was associated with increased mortality (OR 2.0, 95% CI 1.3-3.2) but VAP was not (OR 1.1, 95% CI 0.5-2.4). VAC assessment was faster (mean 1.8 versus 39 minutes per patient). Both VAP and VAC events were predominantly attributable to pneumonia, pulmonary edema, ARDS, and atelectasis.Screening ventilator settings for VAC captures a similar set of complications to traditional VAP surveillance but is faster, more objective, and a superior predictor of outcomes

Automated Identification of Acute Hepatitis B Using Electronic Medical Record Data to Facilitate Public Health Surveillance

Automatic identification of notifiable diseases from electronic medical records can potentially improve the timeliness and completeness of public health surveillance. We describe the development and implementation of an algorithm for prospective surveillance of patients with acute hepatitis B using electronic medical record data.Initial algorithms were created by adapting Centers for Disease Control and Prevention diagnostic criteria for acute hepatitis B into electronic terms. The algorithms were tested by applying them to ambulatory electronic medical record data spanning 1990 to May 2006. A physician reviewer classified each case identified as acute or chronic infection. Additional criteria were added to algorithms in serial fashion to improve accuracy. The best algorithm was validated by applying it to prospective electronic medical record data from June 2006 through April 2008. Completeness of case capture was assessed by comparison with state health department records.A final algorithm including a positive hepatitis B specific test, elevated transaminases and bilirubin, absence of prior positive hepatitis B tests, and absence of an ICD9 code for chronic hepatitis B identified 112/113 patients with acute hepatitis B (sensitivity 97.4%, 95% confidence interval 94-100%; specificity 93.8%, 95% confidence interval 87-100%). Application of this algorithm to prospective electronic medical record data identified 8 cases without false positives. These included 4 patients that had not been reported to the health department. There were no known cases of acute hepatitis B missed by the algorithm.An algorithm using codified electronic medical record data can reliably detect acute hepatitis B. The completeness of public health surveillance may be improved by automatically identifying notifiable diseases from electronic medical record data

Knowledge, attitude, and practices with respect to disease surveillance among urban private practitioners in Pune, India

BACKGROUND: Participation of private practitioners in routine disease surveillance in India is minimal despite the fact that they account for over 70% of the primary healthcare provision. We aimed to investigate the knowledge, attitudes, and practices of private practitioners in the city of Pune toward disease surveillance. Our goal was to identify what barriers and facilitators determine their participation in current and future surveillance efforts. DESIGN: A questionnaire-based survey was conducted among 258 practitioners (response rate 86%). Data were processed using SPSS™ Inc., Chicago, IL, USA, version 17.0.1. RESULTS: Knowledge regarding surveillance, although limited, was better among allopathy practitioners. Surveillance practices did not differ significantly between allopathy and alternate medicine practitioners. Multivariable logistic regression suggested practicing allopathy [odds ratio (OR) 3.125, 95% confidence interval (CI) 1.234–7.915, p=0.016] and availability of a computer (OR 3.670, 95% CI 1.237–10.889, p=0.019) as significant determinants and the presence of a laboratory (OR 3.792, 95% CI 0.998–14.557, p=0.052) as a marginal determinant of the practitioner's willingness to participate in routine disease surveillance systems. Lack of time (137, 55%) was identified as the main barrier at the individual level alongside inadequately trained subordinate staff (14, 6%). Main extrinsic barriers included lack of cooperation between government and the private sector (27, 11%) and legal issues involved in reporting data (15, 6%). There was a general agreement among respondents (239, 94%) that current surveillance efforts need strengthening. Over a third suggested that availability of detailed information and training about surveillance processes (70, 33%) would facilitate reporting. CONCLUSIONS: The high response rate and the practitioners’ willingness to participate in a proposed pilot non-communicable disease surveillance system indicate that there is a general interest from the private sector in cooperating. Keeping reporting systems simple, preferably in electronic formats that minimize infrastructure and time requirements on behalf of the private practitioners, will go a long way in consolidating disease surveillance efforts in the state. Organizing training sessions, providing timely feedback, and awarding continuing medical education points for routine data reporting seem feasible options and should be piloted

Data-driven approach for creating synthetic electronic medical records

<p>Abstract</p> <p>Background</p> <p>New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.</p> <p>Methods</p> <p>This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.</p> <p>Results</p> <p>We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.</p> <p>Conclusions</p> <p>A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.</p