Parents' Trigger Tool for Children with Medical Complexity – PAT‐CMC: Development of a recognition tool for clinical deterioration at home

Abstract

Aim

To develop a trigger tool for parents and lay caregivers of children with medical complexity (CMC) at home and to validate its content.

Design

This was a multi-method study, using qualitative data, a Delphi method and a concept mapping approach.

Methods

A three-round electronic Delphi was performed from December 2021 to April 2022 with a panel of 23 expert parents and 30 healthcare providers, supplemented by a preliminary qualitative exploration of children's signs of deterioration and three consensus meetings to develop the PArents' Trigger Tool for Children with Medical Complexity (PAT-CMC). Cognitive interviews with parents were performed to assess the comprehensiveness and comprehensibility of the tool. The COREQ checklist, the COSMIN guidelines and the CREDES guidelines guided the reporting respectively of the qualitative study, the development and content validity of the trigger tool and the Delphi study.

Results

The PAT-CMC was developed and its content validated to recognize clinical deterioration at home. The tool consists of 7 main clusters of items: Breathing, Heart, Devices, Behaviour, Neuro-Muscular, Nutrition/Hydration and Other Concerns. A total of 23 triggers of deterioration were included and related to two recommendations for escalation of care, using a traffic light coding system.

Conclusion

Priority indicators of clinical deterioration of CMC were identified and integrated into a validated trigger tool designed for parents or other lay caregivers at home, to recognize signs of acute severe illness and initiate healthcare interventions.

Impact

The PAT-CMC was developed to guide families in recognizing signs of deterioration in CMC and has potential for initiating an early escalation of care. This tool may also be useful to support education provided by healthcare providers to families before hospital discharge.

Patient or Public Contribution

Parents of CMC were directly involved in the selection of relevant indicators of children's clinical deterioration and the development of the trigger tool. They were not involved in the design, conducting, reporting or dissemination plans of this research.

Validation of the prevalence to incidence conversion method for healthcare associated infections in long-term care facilities

by Costanza Vicentini, Enrico Ricchizzi, Antonino Russotto, Stefano Bazzolo, Catia Bedosti, Valentina Blengini, Dario Ceccarelli, Elisa Fabbri, Dario Gamba, Anna Maddaleno, Edoardo Rolfini, Margherita Tancredi, Carla Maria Zotti

Residents of long-term care facilities (LTCFs) are a population at high risk of developing severe healthcare associated infections (HAIs). In the assessment of HAIs in acute-care hospitals, selection bias can occur due to cases being over-represented: patients developing HAIs usually have longer lengths of stays compared to controls, and therefore have an increased probability of being sampled in PPS, leading to an overestimation of HAI prevalence. Our hypothesis was that in LTCFs, the opposite may occur: residents developing HAIs either may have a greater chance of being transferred to acute-care facilities or of dying, and therefore could be under-represented in PPS, leading to an underestimation of HAI prevalence. Our aim was to test this hypothesis by comparing HAI rates obtained through longitudinal and cross-sectional studies.

Results from two studies conducted simultaneously in four LTCFs in Italy were compared: a longitudinal study promoted by the European Centre for Disease Prevention and Control (ECDC, HALT4 longitudinal study, H4LS), and a PPS. Prevalence was estimated from the PPS and converted into incidence per year using an adapted version of the Rhame and Sudderth formula proposed by the ECDC. Differences between incidence rates calculated from the PPS results and obtained from H4LS were investigated using the Byar method for rate ratio (RR).

On the day of the PPS, HAI prevalence was 1.47% (95% confidence interval, CI 0.38–3.97), whereas the H4LS incidence rate was 3.53 per 1000 patient-days (PDs, 95% CI 2.99–4.08). Conversion of prevalence rates obtained through the PPS into incidence using the ECDC formula resulted in a rate of 0.86 per 1000 PDs (95% CI 0–2.68). Comparing the two rates, a RR of 0.24 (95% CI 0.03–2.03, p 0.1649) was found.

This study did not find significant differences between HAI incidence estimates obtained from a longitudinal study and through conversion from PPS data. Results of this study support the validity of the ECDC method.