296 Case Report: Pacemaker Lead-Induced Fibrosis Resulting in Right Atrial and Tricuspid Stenosis Managed with An Open Surgical Approach

Pacemaker leads can result in localised inflammation and, over time, fibrosis. Rarely, this can significantly alter the anatomy of the heart and impair cardiac function. In this case, a fifty-year-old female had undergone pacemaker placement in her teens having experienced symptomatic bradycardia. Due to pacemaker pocket erosion, she had undergone a lead extraction where lead fragments had been left in-situ. Years after a new generator and leads were placed, she presented with symptoms of proximal venous congestion and superior vena cava (SVC) syndrome. A venogram demonstrated completely occluded brachiocephalic and innominate veins with significant adjacent venous collateralization. Computed tomography showed partial obstruction of the SVC and tricuspid stenosis. Initially, a decision was made not to intervene.

After developing abdominal distension, she was diagnosed with hepatic congestion and cirrhosis secondary to elevated right sided pressures and right atrial congestion due to tricuspid stenosis. It was concluded that the patient’s symptoms were the result of occluded proximal veins, SVC syndrome, and functional tricuspid stenosis, all of which were likely the result of fibrotic tissue secondary to pacemaker lead-induced inflammation. Due to the severity of her symptoms, the patient accepted the risks associated with surgical management. Intra-operatively, electrocautery was used to debride the fibrotic tissue inhibiting the leaflets of the tricuspid valve. This worked to great effect and additional valve repair/replacement was not necessary. Whilst the patient has been left with SVC syndrome, her tricuspid stenosis symptoms are greatly improved. To our knowledge, such a case has not been previously described.

CD4 cell count at initiation of ART, long-term likelihood of achieving CD4 >750 cells/mm3 and mortality risk

OBJECTIVES

We sought to evaluate associations between CD4 at ART initiation (AI), achieving CD4 >750 cells/mm(3) (CD4 >750), long-term immunological recovery and survival.

METHODS

This was a prospective observational cohort study. We analysed data from ART-naive patients seen in 1996-2012 and followed ≥3 years after AI. We used Kaplan-Meier (KM) methods and log-rank tests to compare time to achieving CD4 >750 by CD4 at AI (CD4-AI); and Cox regression models and generalized estimating equations to identify factors associated with achieving CD4 >750 and mortality risk.

RESULTS

Of 1327 patients, followed for a median of 7.9 years, >85% received ART for ≥75% of follow-up time; 64 died. KM estimates evaluating likelihood of CD4 >750 during 5 years of follow-up, stratified by CD4-AI <50, 50-199, 200-349, 350-499 and 500-750, were 20%, 25%, 56%, 80% and 87%, respectively (log-rank P < 0.001). In adjusted models, CD4-AI ≥200 (versus CD4-AI <200) was associated with achievement of CD4 >750 [adjusted HR (aHR) = 4.77]. Blacks were less likely than whites to achieve CD4 >750 (33% versus 49%, aHR = 0.77). Mortality rates decreased with increasing CD4-AI (P = 0.004 across CD4 strata for AIDS causes and P = 0.009 for non-AIDS death causes). Among decedents with CD4-AI ≥50, 56% of deaths were due to non-AIDS causes.

CONCLUSIONS

Higher CD4-AI resulted in greater long-term CD4 gains, likelihood of achieving CD4 >750, longer survival and decreased mortality regardless of cause. Over 80% of persons with CD4-AI ≥350 achieved CD4 >750 by 4 years while 75% of persons with CD4-AI <200 did not. These data confirm the hazards of delayed AI and support early AI.

HIV Lipodystrophy Case Definition using Artificial Neural Network Modelling

Objective

A case definition of HIV lipodystrophy has recently been developed from a combination of clinical, metabolic and imaging/body composition variables using logistic regression methods. We aimed to evaluate whether artificial neural networks could improve the diagnostic accuracy.

Methods

The database of the case-control Lipodystrophy Case Definition Study was split into 504 subjects (265 with and 239 without lipodystrophy) used for training and 284 independent subjects (152 with and 132 without lipodystrophy) used for validation. Back-propagation neural networks with one or two middle layers were trained and validated. Results were compared against logistic regression models using the same information.

Results

Neural networks using clinical variables only (41 items) achieved consistently superior performance than logistic regression in terms of specificity, overall accuracy and area under the ROC curve. Their average sensitivity and specificity were 72.4 and 71.2%, as compared with 73.0 and 62.9% for logistic regression, respectively (area under the ROC curve, 0.784 vs 0.748). The discriminating performance of the neural networks was largely unaffected when built excluding 13 parameters that patients may not have readily available. The average sensitivity and specificity of the neural networks remained the same when metabolic variables were also considered (total 60 items) without a clear advantage against logistic regression (overall accuracy 71.8%). The performance of networks considering also body composition variables was similar to that of logistic regression (overall accuracy 78.5% for both).

Conclusions

Neural networks may offer a means to improve the discriminating performance for HIV lipodystrophy, when only clinical data are available and a rapid approximate diagnostic decision is needed. In this context, information on metabolic parameters is apparently not helpful in improving the diagnosis of HIV lipodystrophy, unless imaging and body composition studies are also obtained.