We selected observational studies or controlled trials meeting the following inclusion criteria: (1) adult patients receiving invasive mechanical ventilation; (2) pleural effusion confirmed by any imaging modality; (3) thoracentesis or placement of a catheter or tube to drain the pleural effusion; and, (4) clinical outcomes or physiological outcomes or complications reported. Clinical outcomes included duration of mechanical ventilation (primary outcome), mortality, ICU and hospital length of stay, and new clinical management actions based on pleural fluid analysis. Physiological outcomes included changes in oxygenation (ratio of partial pressure of oxygen in systemic arterial blood (PO) to inspired fraction of oxygen (FO), alveolar-arterial gradient of PO, shunt fraction) and lung mechanics (peak inspiratory pressure, plateau pressure, tidal volume, respiratory rate, dynamic compliance). We recorded the occurrence of pneumothorax and hemothorax and other reported complications. We considered studies enrolling both mechanically ventilated and non-ventilated patients for inclusion if outcomes were reported separately for the mechanically ventilated subgroup. We excluded single case reports and studies of patients with pleural effusions that had absolute indications for drainage (for example, empyema, hemothorax, and so on). Each potential study was reviewed for eligibility in duplicate and independently by two authors (ECG, JAL); agreement between reviewers was assessed using Cohen's κ [ 19 ]. Disagreements were resolved by consensus and consultation with a third author (NDF) when necessary.
We collected data on patient demographics, admission diagnosis and severity of illness; study objective, setting, and design; ventilator settings; classification of pleural effusion (exudative vs. transudative); technique of drainage, including the use of imaging guidance, the level of training of the operator, and the type of drainage procedure performed; and outcomes. Only outcomes reported in mechanically ventilated patients were abstracted. For physiologic outcomes, we abstracted outcomes data and time of data collection before and after effusion drainage (see Additional file
1
for details [
20
]). One author (ECG) qualitatively assessed methodological quality based on the Newcastle-Ottawa Scale [
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] and the guidelines developed by the MOOSE working group [
22
].
We aggregated outcomes data at the study level and performed statistical calculations with Review Manager (RevMan) 5.0 (2009; The Cochrane Collaboration, Oxford, UK) using random-effects models [], which incorporate both within-study and between-study variation and generally provide more conservative effect estimates when heterogeneity is present. Data were pooled using the generic inverse variance method, which weights each study by the inverse of the variance of its effect estimate; the weight is adjusted in the presence of between-study heterogeneity. We verified analyses and constructed forest plots using the R statistical package, version 2.7.2 []. All statistical tests were two-sided. We considered < 0.05 as statistically significant in all analyses and report individual trial and summary results with 95% confidence intervals (CIs).
The basic principles of the Moran process . The Moran process assumes that, over short periods of time, the total population of cells is constant. (a) To start with, a cell is selected for reproduction. Selection is dependent on the frequency of the cell in the population and its reproductive fitness (). (b) The cell divides, and the number of cells increases by one. (c,d) Therefore, another cell is selected for export (c) , which returns the population to its normal level (d) .
Evolutionary dynamics under the Moran process . (a) When normal cells divide, there is a probability that one of the daughter cells will have a mutation, while with probability 1 - no mutation occurs. Mutant cell replication increases the number of mutant cells - no back mutations are allowed. (b) Cells have a relative reproductive fitness compared to normal cells, which have a fitness 1. (c) The probability that a cell is chosen for reproduction ( for mutant and for normal cells) is dependent both on its frequency and its relative fitness. If is the number of mutant cells at that time and is the total number of cells present, the number of normal cells will be - . Since a cell has to be chosen at any time - if a mutant cell is not chosen for reproduction, a normal cell will be chosen.
The outcomes of Moran dynamics . (a,d) Assuming that a mutant stem cell is present, stochastic dynamics will predict extinction of the mutant cell (a) or fixation (d) . (b,c) However, fixation may require a long time - hence the clone may persist in a latent state (no disease) (b) or could reach a threshold leading to a disease state (c) . At any of these steps, stochastic extinction is still possible, although less likely as the burden of mutant cells increases. Once the mutant clone reaches fixation (d) this is irreversible. Hence, the only two stable states are extinction or fixation.
Probability distribution functions to reach the diagnostic threshold . Stochastic simulations of Moran dynamics, recording the probability that the mutant clone reaches the diagnostic threshold at a given time after the occurrence of the mutation (diagnosis is here defined as at least 20% of the cells being mutated) as a function of the fitness advantage () of mutated cells. The smaller the fitness advantage of mutated cells, the longer it takes for the threshold to be reached, and the smaller the probability of reaching the threshold in a given time.
The Moran model assumes homogenous mixing of populations; that is, the spatial distribution of the population is not considered. This means that one cell in a specific place reproduces and a cell elsewhere is chosen for death, a scenario perhaps easiest to accept in small populations of stem cells, such as in an individual colonic crypt [
23
]. However, the Moran process may also be of relevance to hematopoiesis, where stem cells are distributed throughout the bone marrow, since HSCs appear to be coupled chemically and perhaps even neurologically [
24
,
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], which may allow them to function as a homogenous population. Perhaps the best example of this tight coupling is the constant frequency of oscillations in a disease known as cyclic hematopoiesis (neutropenia). In this condition, the neutrophil count (and that of other types of cell) oscillates with a regular frequency (19 to 21 days) for the lifetime of the person [
26
]. For this process to be sustained, tight coupling of cellular reproduction in time must be present, even though the cells are scattered in space - otherwise the oscillations will dissipate as cell reproduction loses synchronization [
10
]. On the other hand, Moran dynamics introduced here does not capture the symmetric or asymmetric division of individual cells and therefore the process can only provide an average account of the population dynamics. However, one can argue that it is the population that evolves and not the individual cell(s). An analysis of the impact of the symmetry of cell division on mutant clone dynamics has been described elsewhere [
22
].
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