Adoption in Canada of an international risk scoring tool to predict respiratory syncytial virus hospitalization in moderate-to-late preterm infants

Respiratory Syncytial Virus Infections in Neonates: A Persisting Problem

Respiratory syncytial virus (RSV) is the most common cause of lower respiratory tract infections in young infants. It is an enveloped, single-stranded, nonsegmented, negative-strand RNA virus, a member of the family Pneumoviridae. Globally, RSV is responsible for 2.3% of deaths among neonates 0-27 days of age. Respiratory syncytial virus infection is most common in children aged below 24 months. Neonates present with cough and fever. Respiratory syncytial virus-associated wheezing is seen in 20% infants during the first year of life of which 2-3% require hospitalization. Reverse transcriptase polymerase chain reaction (RT-PCR) gives fast results and has higher sensitivity compared with culture and rapid antigen tests and are not affected by passively administered antibody to RSV. Therapy for RSV infection of the LRT is mainly supportive, and preventive measures like good hygiene and isolation are the mainstay of management. Standard precautions, hand hygiene, breastfeeding and contact isolation should be followed for RSV-infected newborns. Recent AAP guidelines do not recommend pavilizumab prophylaxis for preterm infants born at 29-35 weeks without chronic lung disease, hemodynamically significant congenital heart disease and coexisting conditions. RSV can lead to long-term sequelae such as wheezing and asthma, associated with increased healthcare costs and reduced quality of life.

Impact of using the International Risk Scoring Tool on the cost-utility of palivizumab for preventing severe respiratory syncytial virus infection in Canadian moderate-to-late preterm infants

BACKGROUND AND OBJECTIVE

To assess the cost-utility of palivizumab versus no prophylaxis in preventing severe respiratory syncytial virus (RSV) infection in Canadian moderate-to-late preterm (32-35 weeks' gestational age) infants using an: i) International Risk Scoring Tool (IRST); ii) Canadian RST (CRST).

METHODS

A decision tree was developed to assess cost-utility. Infants assessed at moderate- and high-risk of RSV-related hospitalization (RSVH) by the IRST or CRST received palivizumab or no prophylaxis and then progressed to either: i) RSVH; ii) emergency room/outpatient medically attended RSV-infection (MARI); or, iii) were uninfected/non-medically attended. Infants admitted to intensive care could incur mortality (0.43%). Respiratory morbidity was accounted in all uninfected surviving infants for 6 years or 18 years (RSVH/MARI). Palivizumab efficacy (72.2% RSVH reduction) and hospital outcomes were from the Canadian CARESS, PICNIC and RSV-Quebec studies. Palivizumab costs (50mg: CAN$752; 100mg: $1,505) were calculated from Canadian birth statistics combined with a growth algorithm. Healthcare/payer and societal costs (May 2022; 1.5% discounting) were included.

RESULTS

Cost per quality-adjusted life year (QALY) was $29,789 with the IRST (0.79 probability of being <$50,000) and $15,833 with the CRST (0.96 probability). The model was most sensitive to utility scores, long-term sequelae, and palivizumab cost. Vial sharing improved the incremental cost-utility ratio (IRST: $22,319; CRST: $9,231).

CONCLUSIONS

Palivizumab was highly cost-effective (vs no prophylaxis) in Canadian moderate-to-late preterm infants using either the IRST or CRST. The IRST has fewer risk factors than the CRST (3 vs 7, respectively), captures more potential RSVHs (85% vs 54%) and provides another option to guide cost-effective RSV prophylaxis in Canada.

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.