The only eligible r and omised clinical trial did not demonstrate any consistent effects of NIRS plus a guideline on the assessed clinical outcomes .
The trial was , however , only powered to detect difference in cerebral oxygenation , not morbidities or mortality .
BACKGROUND Cerebral injury and long-term neurodevelopmental impairment is common in extremely preterm infants .
Cerebral near-infrared spectroscopy ( NIRS ) enables continuous estimation of cerebral oxygenation .
This diagnostic method coupled with appropriate interventions if NIRS is out of normal range ( that is cerebral oxygenation within the 55 % to 85 % range ) may offer benefits without causing more harms .
Therefore , NIRS coupled with appropriate responses to abnormal findings on NIRS needs assessment in a systematic review of r and omised clinical trials and quasi-r and omised studies .
OBJECTIVES To evaluate the benefits and harms of interventions that attempt to alter cerebral oxygenation guided by cerebral NIRS monitoring in order to prevent cerebral injury , improve neurological outcome , and increase survival in preterm infants born more than 8 weeks preterm .
Fusing Randomized Trials With Big Data: The Key to Self-learning Health Care Systems?
R and omized clinical trials ( RCTs ) have revolutionized medicine by providing evidence on the efficacy and safety of drugs , devices , and procedures . Today , more than 40 000 RCTs are reported annually , their quality continues to increase , and oversight mechanisms ensure adequate protection of participants . However , RCTs have at least 4 related problems : ( 1 ) they are too expensive and difficult ; ( 2 ) their findings are too broad ( average treatment effect not representative of benefit for any given individual ) and too narrow ( trial population and setting not representative of general practice ) ; ( 3 ) r and omizing patients can make patients and physicians uncomfortable , especially when comparing different types of existing care ; and ( 4 ) there are often long delays before RCT results diffuse into practice . The new alternative is “ big data . ” Because medical care is increasingly digitized in electronic health record ( EHR ) data sets and linked biological and genetic data banks , proponents suggest that health care systems are at the dawn of an era in which a patient ’s prognosis and optimal therapy will be generated from rapid analysis of these data sets using sophisticated machinelearning strategies . The information is relatively inexpensive , generated as a by-product of patient care ( overcoming the cost problem ) , and both specific to individuals ( ie , adequately narrow ) and , en masse , descriptive of the entire delivery system ( ie , adequately broad ) . No individuals are r and omized , so the ethical issues appear less complex . The richness and immediacy of these new data could allow tailored treatment decisions in real time , overcoming delays in knowledge translation . As such , although the RCT remains the gold st and ard for evaluation of experimental therapies , big data is proposed as a better approach for the broad swath of comparative effectiveness questions that arise in clinical practice . Indeed , the Institute of Medicine envisions big data as the engine for so-called learning health care systems.1 Yet big data is not a replacement for the RCT . The singular beauty of the RCT is the strength of causal inference that arises from r and om assignment . All known and unknown factors that can influence outcome , other than the treatment assignment , are distributed r and omly— and , if r and omization is effective , evenly — among the groups . Thus , any statistical difference in outcome between groups can be attributed to the assigned interventions . In contrast , big data exploits variation that may appear r and om but is not in fact r and omized . For example , big data may compare patients who did or did not receive a particular drug and then attempt to determine the extent to which any difference in patient outcome may be related to receiving the drug . However , for all the allure of inexpensive access to massive amounts of data , the Achilles ’ heel is lack of causal inference . No matter how detailed the measurement and how sophisticated the adjustment for all known variables , big data can not eliminate unmeasured factors coincident with a particular treatment assignment that could explain an apparent change in outcome .2 Thus , each approach has complementary strengths : RCTs offer causal inference , and big data offers the potential for low-cost , high-volume , nuanced answers with immediate feedback . Rather than debate which is better , the greatest promise may come from fusing them
The only eligible r and omised clinical trial did not demonstrate any consistent effects of NIRS plus a guideline on the assessed clinical outcomes .
The trial was , however , only powered to detect difference in cerebral oxygenation , not morbidities or mortality .
BACKGROUND Cerebral injury and long-term neurodevelopmental impairment is common in extremely preterm infants .
Cerebral near-infrared spectroscopy ( NIRS ) enables continuous estimation of cerebral oxygenation .
This diagnostic method coupled with appropriate interventions if NIRS is out of normal range ( that is cerebral oxygenation within the 55 % to 85 % range ) may offer benefits without causing more harms .
Therefore , NIRS coupled with appropriate responses to abnormal findings on NIRS needs assessment in a systematic review of r and omised clinical trials and quasi-r and omised studies .
OBJECTIVES To evaluate the benefits and harms of interventions that attempt to alter cerebral oxygenation guided by cerebral NIRS monitoring in order to prevent cerebral injury , improve neurological outcome , and increase survival in preterm infants born more than 8 weeks preterm .