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Objective Patients with severe infection or sepsis require fast identification of the focus and prompt eradication. This study aims at investigating the role of body computed tomography (CT) and identifying outcome predictors in a general ward setting of patients with obscure infection. Methods We retrospectively identified 196 consecutive body CTs acquired in 179 patients with obscure infection, i.e. severe infection or sepsis from general wards with unclear focus, over 12-months in the year 2018. Reports were extracted using a full-text search in the radiological information system (RIS) of a large university medical center. CT reports were classified according to diagnostic confidence of the reader (i.e. certain, likely, possible, no focus), and correlated with clinical and laboratory parameters. The discharge diagnosis was set as the diagnostic reference standard. Contingency tables were prepared for statistical analysis with Chi-squared test amongst other analyses and the calculation of AUC statistics. Results In 133 out of 196 (67.9 %) body CTs from general wards with severe infection or sepsis, body CT identified an infectious focus. 90 % of the infections were located in the chest, abdomen, and genitourinary tract, in descending order. In 76.5 % (150 of 196) of examinations, CT correctly predicted the final infectious source. The positive predictive value (PPV) of a CT-detected focus was 84.2 % (95 % CI 79.0%–88.3%). A high diagnostic confidence of the reader resulted in a PPV of 96.4 % (95 % CI 87.4%–99.1%) while a low confidence resulted in a PPV of 63.3 % (95 % CI 48.2%–76.3%). Conclusion In patients with obscure infection treated in general wards, body CT detects the infectious source with a high positive predictive value. Focus detection accuracy highly depends on the diagnostic confidence of the CT reader.

Aim: Remote follow-up (FU) of implantable cardiac defibrillators (ICDs) allows for fewer in-office visits in combination with earlier detection of relevant findings. Its implementation requires investment and reorganization of care. Providers (physicians or hospitals) are unsure about the financial impact. The primary end-point of this randomized prospective multicentre health economic trial was the total FU-related cost for providers, comparing Home Monitoring facilitated FU (HM ON) to regular in-office FU (HM OFF) during the first 2 years after ICD implantation. Also the net financial impact on providers (taking national reimbursement into account) and costs from a healthcare payer perspective were evaluated. Methods and results: Atotal of 312 patients with VVI-or DDD-ICD implants from 17 centres in six EU countries were randomised to HMON or OFF, of which 303 were eligible for data analysis. For all contacts (in-office, calendar-or alert-triggered web-based review, discussions, calls) time-expenditure was tracked. Country-specific cost parameters were used to convert resource use into monetary values. Remote FU equipment itself was not included in the cost calculations. Given only two patients from Finland (one in each group) a monetary valuation analysis was not performed for Finland. Average age was 62.4 +/- 13.1 years, 81% were male, 39% received a DDD system, and 51% had a prophylactic ICD. Resource use with HM ON was clearly different: less FU visits (3.79 +/- 1.67 vs. 5.53 +/- 2.32; P < 0.001) despite a small increase of unscheduled visits (0.95 +/- 1.50 vs. 0.62 +/- 1.25; P < 0.005), more non-office-based contacts (1.95+3.29 vs. 1.01 +/- 2.64; P < 0.001), more Internet sessions (11.02 +/- 15.28 vs. 0.06 +/- 0.31; P < 0.001) and more in-clinic discussions (1.84 +/- 4.20 vs. 1.28 +/- 2.92; P < 0.03), but with numerically fewer hospitalizations (0.67 +/- 1.18 vs. 0.85 +/- 1.43, P = 0.23) and shorter length-of-stay (6.31 +/- 15.5 vs. 8.26 +/- 18.6; P = 0.27), although not significant. For the whole study population, the total FU cost for providers was not different for HM ON vs. OFF [mean (95% CI): (sic)204 169-238) vs. (sic)213 (182-243); range for difference ((sic)-36 to 54), NS]. From a payer perspective, FU-related costs were similar while the total cost per patient (including other physician visits, examinations, and hospitalizations) was numerically (but not significantly) lower. There was no difference in the net financial impact on providers [profit of (sic)408 (327-489) vs. (sic)400 (345-455); range for difference ((sic)-104 to 88), NS], but there was heterogeneity among countries, with less profit for providers in the absence of specific remote FU reimbursement (Belgium, Spain, and the Netherlands) and maintained or increased profit in cases where such reimbursement exists (Germany and UK). Quality of life (SF-36) was not different. Conclusion: For all the patients as a whole, FU-related costs for providers are not different for remote FU vs. purely in-office FU, despite reorganized care. However, disparity in the impact on provider budget among different countries illustrates the need for proper reimbursement to ensure effective remote FU implementation.

Aim Automated, daily Home Monitoring (HM) of pacemaker and implantable cardioverter-defibrillator (ICD) patients can improve patient care. Yet, HM introduction to routine clinical practice is challenged by resource allocation for regular HM data review. We tested the feasibility, safety, workload, and clinical usefulness of a centralized HM model consisting of one monitor centre and nine satellite clinics. Methods and results Having no knowledge about patients' clinical data, a telemonitoring nurse (TN) and a supporting physician at the monitor centre screened and filtered HM data in 62 pacemaker and 59 ICD patients from nine satellite clinics for over 1 year. Basic screening of arrhythmic and technical events required 25.7 min (TN) and 0.7 min (physician) per working day, normalized for 100 patients monitored. Communication of relevant events to satellite clinics per email or phone required additional 4.3 min (TN) and 0.4 min (physician). Telemonitoring nurse also screened for abnormal developments in longitudinal data trends weekly for 3 months after implantation, and then monthly; one patient session lasted 4.0 ± 2.9 min. To handle transmission-gap notifications, TN needed additional 2.8 min daily. Satellite clinics received 231.3 observations from the monitor centre per 100 patients/year, which prompted 86.3 patient contacts or intensive HM screening periods by the satellite clinic itself (37.3% response rate), 51.7 extra follow-up controls (22.3%), and 30.1 clinical interventions (13.0%). Conclusion Centralized HM was feasible, reliable, safe, and clinically useful. Basic screening and communication of relevant arrhythmic and technical events required a total of 30 min (TN) and 1.1 min (physician) daily per 100 patients monitored.

A 56-year-old woman complained about episodes of pre-syncope. Following ECG diagnosis of 2 : 1 conducting AV-block II°, she was referred to our cardiology department. DDD pacemaker implantation was performed, and the patient was discharged free of symptoms. A routine chest X-ray confirmed the correct position of both atrial and …